This R-markdown document contains all r-code used to carry out the analysis for the paper on fish catch rates and biodiversity along the Red Sea coast of Sudan. The analysis is based on three surveys: November 2012, May 2013 and November 2013.
Here the analysis is structured around the 7 management regions of the Sudanese Red Sea coast as follows:
All code and data are stored on GitHub
Reading catch, station and traits data.
Neither station data, nor catch data has a complete depth record, but by combining depth para from each we can get a complete depth parameter for all stations.
Loads the map data for Sudan, loads the management areas from shape-file etc.
## OGR data source with driver: ESRI Shapefile
## Source: "/Users/eriko/GitHub/Sudan2019/Sudan-master/sudan_management_areas", layer: "sudan_regions"
## with 7 features
## It has 3 fields
## Integer64 fields read as strings: id
Adds ‘Area’ to each line in the catch table.
Of the catch data, 12 fish registrations lack weight, (i.e. this was forgotten entered into the database during the survey). These registrations, would if included lead to 12 more registrations of CPUE = 0.
Some more data wrangling that adds the traits from the traits table to the catch data, using only the station with catches (there are no traits for ‘NOCATCH’ species).
Also, only select traps, gillnets and handlines as these were the only gear with sufficient numbers and consistent use to be analyzed.
Lastly adds number of gear deployed at each station to each line.
A nice, bathymetric map of Sudan with management areas overlaid. (Fig. 1 in MS)
March 2021: New code for making bathymetric map to include depth legend, north arrow and scale bar.
## quartz_off_screen
## 2
Faceted map plotting position of all catch stations for each of the three surveys.
Table describing the sampling effort in each area pr survey, number of different gear types, max, min and average depth, number of traps with / without catches.
| survey | id | Ntraps | Nhl | NGn | TBhrs | HLhrs | GNhrs | DepthAvg | DepthSD | DepthMax | DepthMin |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012901 | 1 | 22 | 0 | 0 | 694.066 | 0.000 | 0.000 | 42.45455 | 27.104256 | 142 | 13 |
| 2012901 | 2 | 54 | 3 | 3 | 721.549 | 78.000 | 62.000 | 40.89744 | 16.597075 | 71 | 0 |
| 2012901 | 3 | 26 | 0 | 1 | 451.183 | 0.000 | 14.000 | 30.80769 | 19.237503 | 95 | 8 |
| 2012901 | 4 | 5 | 0 | 0 | 77.084 | 0.000 | 0.000 | 22.60000 | 17.910891 | 54 | 10 |
| 2012901 | 5 | 31 | 0 | 4 | 677.896 | 0.000 | 78.317 | 21.32258 | 6.498139 | 30 | 7 |
| 2012901 | 6 | 36 | 0 | 1 | 850.417 | 0.000 | 38.250 | 32.38889 | 24.397046 | 88 | 0 |
| 2012901 | 7 | 31 | 0 | 8 | 712.200 | 0.000 | 120.949 | 31.06667 | 17.091908 | 66 | 5 |
| 2013002 | 1 | 29 | 0 | 1 | 420.163 | 0.000 | 12.000 | 31.48148 | 15.282897 | 70 | 5 |
| 2013002 | 2 | 81 | 3 | 5 | 1036.479 | 377.500 | 221.569 | 27.09091 | 13.628846 | 145 | 5 |
| 2013002 | 3 | 32 | 0 | 1 | 525.111 | 0.000 | 12.000 | 28.87500 | 16.163978 | 60 | 0 |
| 2013002 | 4 | 13 | 0 | 10 | 160.266 | 0.000 | 137.815 | 29.50000 | 23.114450 | 67 | 9 |
| 2013002 | 5 | 33 | 2 | 5 | 208.899 | 192.000 | 105.766 | 20.46154 | 10.974329 | 50 | 7 |
| 2013002 | 6 | 45 | 1 | 2 | 642.413 | 156.000 | 78.000 | 34.70270 | 22.067906 | 88 | 9 |
| 2013002 | 7 | 39 | 2 | 0 | 666.410 | 186.000 | 0.000 | 33.46154 | 19.704189 | 76 | 5 |
| 2013005 | 1 | 23 | 1 | 4 | 271.551 | 3.000 | 84.000 | 29.82353 | 13.130286 | 80 | 10 |
| 2013005 | 2 | 57 | 2 | 6 | 500.101 | 111.000 | 156.000 | 38.27273 | 17.718763 | 80 | 7 |
| 2013005 | 3 | 9 | 0 | 2 | 123.099 | 0.000 | 36.000 | 26.50000 | 21.407609 | 70 | 9 |
| 2013005 | 4 | 16 | 2 | 4 | 151.184 | 4.500 | 142.767 | 32.90000 | 17.922363 | 68 | 12 |
| 2013005 | 5 | 30 | 2 | 3 | 317.498 | 7.000 | 146.947 | 25.52381 | 10.424102 | 65 | 11 |
| 2013005 | 6 | 40 | 4 | 2 | 443.983 | 31.501 | 71.915 | 40.14815 | 25.673548 | 89 | 6 |
| 2013005 | 7 | 22 | 0 | 2 | 171.784 | 0.000 | 203.171 | 34.75000 | 16.625365 | 54 | 11 |
Number of fish (organized by family and species) caught by Gillnet or Traps for each survey, and in total across all surveys and gears.
## families species
## 1 40 128
## # A tibble: 2 x 3
## gear families species
## * <chr> <int> <int>
## 1 GN 37 95
## 2 TB 19 67
| fam_name | Sci_name | 2012901_GN | 2012901_TB | 2013002_GN | 2013002_TB | 2013005_GN | 2013005_TB | sum |
|---|---|---|---|---|---|---|---|---|
| ACANTHURIDAE | Acanthurus gahhm | 0 | 14 | 0 | 29 | 7 | 55 | 105 |
| ACANTHURIDAE | Acanthurus nigrofuscus | 0 | 0 | 0 | 18 | 0 | 0 | 18 |
| ALBULIDAE | Albula glossodonta | 0 | 0 | 0 | 0 | 8 | 0 | 8 |
| CARANGIDAE | Alectis indicus | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| CARANGIDAE | Alepes vari | 0 | 0 | 0 | 0 | 5 | 0 | 5 |
| SPARIDAE | Argyrops filamentosus | 0 | 0 | 0 | 9 | 0 | 0 | 9 |
| SPARIDAE | Argyrops sp. | 0 | 25 | 0 | 0 | 0 | 0 | 25 |
| SPARIDAE | Argyrops spinifer | 0 | 0 | 0 | 9 | 0 | 10 | 19 |
| ARIIDAE | Arius thalassinus | 0 | 0 | 0 | 4 | 2 | 0 | 6 |
| SCOMBRIDAE | Auxis thazard | 15 | 0 | 0 | 0 | 0 | 0 | 15 |
| BALISTIDAE | Balistapus undulatus | 0 | 0 | 0 | 0 | 0 | 2 | 2 |
| BALISTIDAE | Balistoides viridescens | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| BOTHIDAE | Bothus pantherinus | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| CAESIONIDAE | Caesio caerulaurea | 0 | 0 | 17 | 0 | 0 | 0 | 17 |
| CAESIONIDAE | Caesio suevica | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| CARANGIDAE | Carangoides armatus | 0 | 0 | 0 | 0 | 5 | 0 | 5 |
| CARANGIDAE | Carangoides bajad | 16 | 2 | 11 | 16 | 62 | 0 | 107 |
| CARANGIDAE | Carangoides ferdau | 0 | 0 | 0 | 2 | 9 | 0 | 11 |
| CARANGIDAE | Carangoides fulvoguttatus | 0 | 0 | 0 | 1 | 22 | 0 | 23 |
| CARANGIDAE | Carangoides sp. | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| CARANGIDAE | Caranx ignobilis | 0 | 0 | 0 | 2 | 2 | 0 | 4 |
| CARANGIDAE | Caranx melampygus | 3 | 0 | 0 | 2 | 8 | 0 | 13 |
| CARANGIDAE | Caranx sexfasciatus | 17 | 0 | 23 | 3 | 50 | 0 | 93 |
| CARANGIDAE | Caranx sp. | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| Carcharhinidae | Carcharhinus albimarginatus | 3 | 0 | 0 | 0 | 0 | 0 | 3 |
| Carcharhinidae | Carcharhinus melanopterus | 10 | 1 | 0 | 0 | 7 | 0 | 18 |
| Carcharhinidae | Carcharhinus wheeleri | 0 | 0 | 2 | 0 | 2 | 0 | 4 |
| ARIIDAE | Carlarius heudelotii | 0 | 10 | 0 | 0 | 0 | 0 | 10 |
| SERRANIDAE | Cephalopholis argus | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| SERRANIDAE | Cephalopholis miniatus | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| SERRANIDAE | Cephaplpholis rogaa | 0 | 4 | 0 | 17 | 0 | 2 | 23 |
| CHAETODONTIDAE | Chaetodon auriga | 0 | 5 | 0 | 0 | 0 | 0 | 5 |
| CHAETODONTIDAE | Chaetodon semilarvatus | 0 | 0 | 17 | 3 | 0 | 0 | 20 |
| CHANIDAE | Chanos chanos | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| LABRIDAE | Cheilinus lunulatus | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| LABRIDAE | Cheilinus quinquecintus | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| CHIROCENTRIDAE | Chirocentrus dorab | 19 | 0 | 20 | 0 | 65 | 0 | 104 |
| PLATYCEPHALIDAE | Cociella crocodilus | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| MUGILIDAE | Crenimugil crenilabis | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| CARANGIDAE | Decapterus macarellus | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| CARANGIDAE | Decapterus russelli | 0 | 0 | 2 | 5 | 0 | 0 | 7 |
| HAEMULIDAE | Diagramma pictum | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| DIODONTIDAE | Diodon hystrix | 0 | 0 | 0 | 0 | 5 | 0 | 5 |
| ECHENEIDIDAE | Echeneis naucrates | 0 | 0 | 0 | 5 | 4 | 0 | 9 |
| CARANGIDAE | Elagatis bipinnulata | 0 | 0 | 9 | 0 | 0 | 0 | 9 |
| SERRANIDAE | Epinephelus chlorostigma | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| SERRANIDAE | Epinephelus fasciatus | 0 | 0 | 0 | 2 | 0 | 2 | 4 |
| SERRANIDAE | Epinephelus fuscoguttatus | 0 | 12 | 0 | 16 | 2 | 4 | 34 |
| SERRANIDAE | Epinephelus summana | 0 | 0 | 3 | 2 | 0 | 0 | 5 |
| SERRANIDAE | Epinephelus tauvina | 2 | 21 | 7 | 8 | 6 | 4 | 48 |
| SCOMBRIDAE | Euthynnus affinis | 0 | 0 | 5 | 0 | 4 | 0 | 9 |
| FISTULARIIDAE | FISTULARIIDAE | 10 | 0 | 0 | 2 | 0 | 0 | 12 |
| GERREIDAE | Gerres oyena | 0 | 0 | 2 | 0 | 8 | 0 | 10 |
| CARANGIDAE | Gnathonodon speciosus | 0 | 0 | 0 | 0 | 5 | 0 | 5 |
| SCOMBRIDAE | Grammatorcynus bilineatus | 29 | 3 | 8 | 0 | 8 | 0 | 48 |
| LETHRINIDAE | Gymnocranius grandoculis | 0 | 0 | 0 | 3 | 4 | 0 | 7 |
| SCOMBRIDAE | Gymnosarda unicolor | 0 | 0 | 0 | 0 | 14 | 0 | 14 |
| MURAENIDAE | Gymnothorax flavimarginatus | 0 | 5 | 0 | 0 | 0 | 0 | 5 |
| MURAENIDAE | Gymnothorax javanicus | 0 | 54 | 1 | 23 | 0 | 2 | 80 |
| HEMIRAMPHIDAE | Hemirhamphus far | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| SCARIDAE | Hipposcarus harid | 0 | 0 | 5 | 0 | 5 | 0 | 10 |
| SCOMBRIDAE | Katsuwonus pelamis | 2 | 0 | 0 | 0 | 0 | 0 | 2 |
| KYPHOSIDAE | Kyphosus vaigiensis | 0 | 0 | 0 | 0 | 13 | 0 | 13 |
| LETHRINIDAE | Lethrinus elongatus | 0 | 17 | 2 | 29 | 8 | 8 | 64 |
| LETHRINIDAE | Lethrinus harak | 0 | 0 | 0 | 0 | 12 | 0 | 12 |
| LETHRINIDAE | Lethrinus lentjan | 4 | 53 | 7 | 73 | 42 | 39 | 218 |
| LETHRINIDAE | Lethrinus mahsena | 0 | 30 | 3 | 36 | 0 | 28 | 97 |
| LETHRINIDAE | Lethrinus microdon | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| LETHRINIDAE | Lethrinus nebulosus | 0 | 0 | 0 | 0 | 0 | 2 | 2 |
| LETHRINIDAE | Lethrinus obsoletus | 0 | 0 | 2 | 2 | 0 | 2 | 6 |
| LETHRINIDAE | Lethrinus xanthochilus | 0 | 0 | 0 | 3 | 0 | 2 | 5 |
| LUTJANIDAE | Lutjanus argentimaculatus | 0 | 0 | 0 | 2 | 0 | 2 | 4 |
| LUTJANIDAE | Lutjanus bohar | 0 | 58 | 16 | 118 | 5 | 15 | 212 |
| LUTJANIDAE | Lutjanus ehrenbergii | 8 | 0 | 15 | 0 | 10 | 0 | 33 |
| LUTJANIDAE | Lutjanus fulviflamma | 0 | 0 | 0 | 0 | 3 | 0 | 3 |
| LUTJANIDAE | Lutjanus gibbus | 0 | 55 | 0 | 80 | 5 | 48 | 188 |
| LUTJANIDAE | Lutjanus kasmira | 0 | 7 | 0 | 6 | 0 | 4 | 17 |
| LUTJANIDAE | Lutjanus monostigma | 3 | 8 | 0 | 6 | 0 | 2 | 19 |
| LUTJANIDAE | Lutjanus rivulatus | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| LUTJANIDAE | Lutjanus sebae | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| LUTJANIDAE | Lutjanus sp. | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| LUTJANIDAE | Macolor niger | 0 | 0 | 0 | 4 | 2 | 0 | 6 |
| LETHRINIDAE | Monotaxis grandoculis | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| MULLIDAE | Mulloidichtys flavolineatus | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| MULLIDAE | Mulloidichtys vanicolensis | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| HOLOCENTRIDAE | Myripristis murdjan | 0 | 0 | 4 | 0 | 6 | 0 | 10 |
| ACANTHURIDAE | Naso elegans | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| ACANTHURIDAE | Naso hexacanthus | 0 | 0 | 19 | 19 | 38 | 0 | 76 |
| NO CATCH | NO CATCH | 5 | 0 | 7 | 0 | 0 | 0 | 12 |
| LUTJANIDAE | Paracaesio sordius | 0 | 3 | 0 | 0 | 0 | 0 | 3 |
| SOLEIDAE | Pardarchius sp. | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| EPHIPPIDAE | Platax boersi | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| EPHIPPIDAE | Platax orbicularis | 0 | 4 | 0 | 3 | 2 | 4 | 13 |
| HAEMULIDAE | Plectorhinchus gaterinus | 0 | 11 | 2 | 0 | 5 | 0 | 18 |
| POMADASYIDAE (HAEMULIDAE) | Plectorhinchus pictus | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| HAEMULIDAE | Plectrohinchus pictus | 0 | 0 | 0 | 7 | 2 | 0 | 9 |
| SERRANIDAE | Plectropomus pessuliferus marisrubri | 0 | 4 | 0 | 4 | 0 | 0 | 8 |
| HAEMULIDAE | Pletrohinchus schotaf | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| PRIACANTHIDAE | Priacanthus hamrur | 0 | 0 | 6 | 0 | 0 | 0 | 6 |
| LUTJANIDAE | Pristipomoides multidens | 0 | 14 | 0 | 9 | 0 | 0 | 23 |
| BALISTIDAE | Pseudobalistes flavimarginatus | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| SCOMBRIDAE | Rastrelliger kanagurta | 0 | 0 | 7 | 0 | 24 | 0 | 31 |
| SCOMBRIDAE | Sarda orientalis | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| HOLOCENTRIDAE | Sargocentron rubrum | 0 | 0 | 0 | 23 | 0 | 7 | 30 |
| HOLOCENTRIDAE | Sargocentron spiniferum | 0 | 24 | 2 | 26 | 2 | 10 | 64 |
| SCARIDAE | Scarus ferrugineus | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| SCARIDAE | Scarus frenatus | 0 | 0 | 3 | 0 | 3 | 0 | 6 |
| SCARIDAE | Scarus ghobban | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| SCOMBRIDAE | Scomber australasicus | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| CARANGIDAE | Scomberoides lysan | 17 | 21 | 66 | 25 | 53 | 0 | 182 |
| CARANGIDAE | Scomberoides tol | 0 | 0 | 5 | 0 | 77 | 0 | 82 |
| SCOMBRIDAE | Scomberomorus commerson | 39 | 0 | 0 | 0 | 12 | 0 | 51 |
| SIGANIDAE | Siganus argenteus | 0 | 0 | 4 | 0 | 0 | 0 | 4 |
| SIGANIDAE | Siganus luridus | 2 | 0 | 2 | 0 | 0 | 0 | 4 |
| SIGANIDAE | Siganus rivulatus | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| SIGANIDAE | Siganus stellatus | 0 | 0 | 2 | 0 | 0 | 3 | 5 |
| SPARIDAE | Sparus sp. | 0 | 0 | 0 | 7 | 0 | 0 | 7 |
| SPHYRAENIDAE | Sphyraena forsteri | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| SPHYRAENIDAE | Sphyraena jello | 5 | 0 | 0 | 0 | 0 | 0 | 5 |
| SPHYRAENIDAE | Sphyraena putnamae | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| SPHYRAENIDAE | Sphyraena qenie | 0 | 0 | 8 | 0 | 7 | 0 | 15 |
| SPHYRNIDAE | Sphyrna lewini | 2 | 0 | 0 | 0 | 0 | 0 | 2 |
| R A Y S | Taeniura lymma | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| SCOMBRIDAE | Thunnus albacares | 12 | 0 | 0 | 0 | 0 | 0 | 12 |
| Carcharhinidae | Triaenodon obesus | 0 | 5 | 0 | 17 | 0 | 0 | 22 |
| BELONIDAE | Tylosurus choram | 3 | 0 | 8 | 0 | 0 | 0 | 11 |
| MUGILIDAE | Valamugil engeli | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| SERRANIDAE | Variola louti | 0 | 0 | 0 | 4 | 0 | 0 | 4 |
| fam_name | Sci_name | 2012901_GN | 2012901_TB | 2013002_GN | 2013002_TB | 2013005_GN | 2013005_TB | sum |
|---|---|---|---|---|---|---|---|---|
| ACANTHURIDAE | Acanthurus gahhm | 0 | 4 | 0 | 5 | 1 | 6 | 16 |
| ACANTHURIDAE | Acanthurus nigrofuscus | 0 | 0 | 0 | 6 | 0 | 0 | 6 |
| ALBULIDAE | Albula glossodonta | 0 | 0 | 0 | 0 | 3 | 0 | 3 |
| CARANGIDAE | Alectis indicus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| CARANGIDAE | Alepes vari | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SPARIDAE | Argyrops filamentosus | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
| SPARIDAE | Argyrops sp. | 0 | 5 | 0 | 0 | 0 | 0 | 5 |
| SPARIDAE | Argyrops spinifer | 0 | 0 | 0 | 3 | 0 | 5 | 8 |
| ARIIDAE | Arius thalassinus | 0 | 0 | 0 | 2 | 1 | 0 | 3 |
| SCOMBRIDAE | Auxis thazard | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| BALISTIDAE | Balistapus undulatus | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| BALISTIDAE | Balistoides viridescens | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| BOTHIDAE | Bothus pantherinus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| CAESIONIDAE | Caesio caerulaurea | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| CAESIONIDAE | Caesio suevica | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| CARANGIDAE | Carangoides armatus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| CARANGIDAE | Carangoides bajad | 3 | 1 | 5 | 2 | 13 | 0 | 24 |
| CARANGIDAE | Carangoides ferdau | 0 | 0 | 0 | 1 | 2 | 0 | 3 |
| CARANGIDAE | Carangoides fulvoguttatus | 0 | 0 | 0 | 1 | 6 | 0 | 7 |
| CARANGIDAE | Carangoides sp. | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| CARANGIDAE | Caranx ignobilis | 0 | 0 | 0 | 1 | 1 | 0 | 2 |
| CARANGIDAE | Caranx melampygus | 1 | 0 | 0 | 1 | 2 | 0 | 4 |
| CARANGIDAE | Caranx sexfasciatus | 1 | 0 | 1 | 1 | 7 | 0 | 10 |
| CARANGIDAE | Caranx sp. | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| Carcharhinidae | Carcharhinus albimarginatus | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| Carcharhinidae | Carcharhinus melanopterus | 1 | 1 | 0 | 0 | 2 | 0 | 4 |
| Carcharhinidae | Carcharhinus wheeleri | 0 | 0 | 1 | 0 | 1 | 0 | 2 |
| ARIIDAE | Carlarius heudelotii | 0 | 4 | 0 | 0 | 0 | 0 | 4 |
| SERRANIDAE | Cephalopholis argus | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| SERRANIDAE | Cephalopholis miniatus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SERRANIDAE | Cephaplpholis rogaa | 0 | 2 | 0 | 7 | 0 | 1 | 10 |
| CHAETODONTIDAE | Chaetodon auriga | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| CHAETODONTIDAE | Chaetodon semilarvatus | 0 | 0 | 1 | 1 | 0 | 0 | 2 |
| CHANIDAE | Chanos chanos | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| LABRIDAE | Cheilinus lunulatus | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| LABRIDAE | Cheilinus quinquecintus | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| CHIROCENTRIDAE | Chirocentrus dorab | 4 | 0 | 3 | 0 | 5 | 0 | 12 |
| PLATYCEPHALIDAE | Cociella crocodilus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| MUGILIDAE | Crenimugil crenilabis | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| CARANGIDAE | Decapterus macarellus | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| CARANGIDAE | Decapterus russelli | 0 | 0 | 1 | 1 | 0 | 0 | 2 |
| HAEMULIDAE | Diagramma pictum | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| DIODONTIDAE | Diodon hystrix | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| ECHENEIDIDAE | Echeneis naucrates | 0 | 0 | 0 | 2 | 2 | 0 | 4 |
| CARANGIDAE | Elagatis bipinnulata | 0 | 0 | 3 | 0 | 0 | 0 | 3 |
| SERRANIDAE | Epinephelus chlorostigma | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| SERRANIDAE | Epinephelus fasciatus | 0 | 0 | 0 | 1 | 0 | 1 | 2 |
| SERRANIDAE | Epinephelus fuscoguttatus | 0 | 5 | 0 | 9 | 1 | 2 | 17 |
| SERRANIDAE | Epinephelus summana | 0 | 0 | 1 | 1 | 0 | 0 | 2 |
| SERRANIDAE | Epinephelus tauvina | 1 | 10 | 3 | 4 | 3 | 2 | 23 |
| SCOMBRIDAE | Euthynnus affinis | 0 | 0 | 1 | 0 | 1 | 0 | 2 |
| FISTULARIIDAE | FISTULARIIDAE | 1 | 0 | 0 | 1 | 0 | 0 | 2 |
| GERREIDAE | Gerres oyena | 0 | 0 | 1 | 0 | 2 | 0 | 3 |
| CARANGIDAE | Gnathonodon speciosus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SCOMBRIDAE | Grammatorcynus bilineatus | 3 | 1 | 3 | 0 | 3 | 0 | 10 |
| LETHRINIDAE | Gymnocranius grandoculis | 0 | 0 | 0 | 1 | 1 | 0 | 2 |
| SCOMBRIDAE | Gymnosarda unicolor | 0 | 0 | 0 | 0 | 4 | 0 | 4 |
| MURAENIDAE | Gymnothorax flavimarginatus | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
| MURAENIDAE | Gymnothorax javanicus | 0 | 26 | 1 | 12 | 0 | 1 | 40 |
| HEMIRAMPHIDAE | Hemirhamphus far | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SCARIDAE | Hipposcarus harid | 0 | 0 | 1 | 0 | 1 | 0 | 2 |
| SCOMBRIDAE | Katsuwonus pelamis | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| KYPHOSIDAE | Kyphosus vaigiensis | 0 | 0 | 0 | 0 | 3 | 0 | 3 |
| LETHRINIDAE | Lethrinus elongatus | 0 | 7 | 1 | 12 | 1 | 3 | 24 |
| LETHRINIDAE | Lethrinus harak | 0 | 0 | 0 | 0 | 2 | 0 | 2 |
| LETHRINIDAE | Lethrinus lentjan | 1 | 16 | 1 | 26 | 5 | 11 | 60 |
| LETHRINIDAE | Lethrinus mahsena | 0 | 14 | 1 | 17 | 0 | 10 | 42 |
| LETHRINIDAE | Lethrinus microdon | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| LETHRINIDAE | Lethrinus nebulosus | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| LETHRINIDAE | Lethrinus obsoletus | 0 | 0 | 1 | 1 | 0 | 1 | 3 |
| LETHRINIDAE | Lethrinus xanthochilus | 0 | 0 | 0 | 1 | 0 | 1 | 2 |
| LUTJANIDAE | Lutjanus argentimaculatus | 0 | 0 | 0 | 1 | 0 | 1 | 2 |
| LUTJANIDAE | Lutjanus bohar | 0 | 26 | 6 | 49 | 2 | 7 | 90 |
| LUTJANIDAE | Lutjanus ehrenbergii | 2 | 0 | 4 | 0 | 3 | 0 | 9 |
| LUTJANIDAE | Lutjanus fulviflamma | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| LUTJANIDAE | Lutjanus gibbus | 0 | 17 | 0 | 29 | 2 | 19 | 67 |
| LUTJANIDAE | Lutjanus kasmira | 0 | 3 | 0 | 3 | 0 | 2 | 8 |
| LUTJANIDAE | Lutjanus monostigma | 1 | 4 | 0 | 3 | 0 | 1 | 9 |
| LUTJANIDAE | Lutjanus rivulatus | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| LUTJANIDAE | Lutjanus sebae | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| LUTJANIDAE | Lutjanus sp. | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| LUTJANIDAE | Macolor niger | 0 | 0 | 0 | 2 | 1 | 0 | 3 |
| LETHRINIDAE | Monotaxis grandoculis | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| MULLIDAE | Mulloidichtys flavolineatus | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| MULLIDAE | Mulloidichtys vanicolensis | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| HOLOCENTRIDAE | Myripristis murdjan | 0 | 0 | 1 | 0 | 2 | 0 | 3 |
| ACANTHURIDAE | Naso elegans | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| ACANTHURIDAE | Naso hexacanthus | 0 | 0 | 1 | 1 | 1 | 0 | 3 |
| NO CATCH | NO CATCH | 5 | 0 | 7 | 0 | 0 | 0 | 12 |
| LUTJANIDAE | Paracaesio sordius | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| SOLEIDAE | Pardarchius sp. | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| EPHIPPIDAE | Platax boersi | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| EPHIPPIDAE | Platax orbicularis | 0 | 2 | 0 | 1 | 1 | 2 | 6 |
| HAEMULIDAE | Plectorhinchus gaterinus | 0 | 4 | 1 | 0 | 2 | 0 | 7 |
| POMADASYIDAE (HAEMULIDAE) | Plectorhinchus pictus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| HAEMULIDAE | Plectrohinchus pictus | 0 | 0 | 0 | 3 | 1 | 0 | 4 |
| SERRANIDAE | Plectropomus pessuliferus marisrubri | 0 | 2 | 0 | 2 | 0 | 0 | 4 |
| HAEMULIDAE | Pletrohinchus schotaf | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| PRIACANTHIDAE | Priacanthus hamrur | 0 | 0 | 2 | 0 | 0 | 0 | 2 |
| LUTJANIDAE | Pristipomoides multidens | 0 | 5 | 0 | 2 | 0 | 0 | 7 |
| BALISTIDAE | Pseudobalistes flavimarginatus | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| SCOMBRIDAE | Rastrelliger kanagurta | 0 | 0 | 1 | 0 | 3 | 0 | 4 |
| SCOMBRIDAE | Sarda orientalis | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| HOLOCENTRIDAE | Sargocentron rubrum | 0 | 0 | 0 | 12 | 0 | 3 | 15 |
| HOLOCENTRIDAE | Sargocentron spiniferum | 0 | 11 | 1 | 13 | 1 | 5 | 31 |
| SCARIDAE | Scarus ferrugineus | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| SCARIDAE | Scarus frenatus | 0 | 0 | 1 | 0 | 1 | 0 | 2 |
| SCARIDAE | Scarus ghobban | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SCOMBRIDAE | Scomber australasicus | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| CARANGIDAE | Scomberoides lysan | 2 | 1 | 6 | 1 | 8 | 0 | 18 |
| CARANGIDAE | Scomberoides tol | 0 | 0 | 1 | 0 | 3 | 0 | 4 |
| SCOMBRIDAE | Scomberomorus commerson | 2 | 0 | 0 | 0 | 3 | 0 | 5 |
| SIGANIDAE | Siganus argenteus | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| SIGANIDAE | Siganus luridus | 1 | 0 | 1 | 0 | 0 | 0 | 2 |
| SIGANIDAE | Siganus rivulatus | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SIGANIDAE | Siganus stellatus | 0 | 0 | 1 | 0 | 0 | 1 | 2 |
| SPARIDAE | Sparus sp. | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| SPHYRAENIDAE | Sphyraena forsteri | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| SPHYRAENIDAE | Sphyraena jello | 2 | 0 | 0 | 0 | 0 | 0 | 2 |
| SPHYRAENIDAE | Sphyraena putnamae | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SPHYRAENIDAE | Sphyraena qenie | 0 | 0 | 2 | 0 | 3 | 0 | 5 |
| SPHYRNIDAE | Sphyrna lewini | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| R A Y S | Taeniura lymma | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SCOMBRIDAE | Thunnus albacares | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| Carcharhinidae | Triaenodon obesus | 0 | 2 | 0 | 7 | 0 | 0 | 9 |
| BELONIDAE | Tylosurus choram | 1 | 0 | 4 | 0 | 0 | 0 | 5 |
| MUGILIDAE | Valamugil engeli | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| SERRANIDAE | Variola louti | 0 | 0 | 0 | 2 | 0 | 0 | 2 |
Box plot of depths at wich traps were set. I prefer this visualization of depth ranges of the traps, rather than a (boring) table.
####Test differneces in depths in each area between years and between areas
##
## Shapiro-Wilk normality test
##
## data: c3$depth
## W = 0.90427, p-value < 2.2e-16
##
## Kruskal-Wallis rank sum test
##
## data: c3$depth and c3$sa
## Kruskal-Wallis chi-squared = 53.895, df = 20, p-value = 5.997e-05
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 53.8945, df = 20, p-value = 0
##
##
## Comparison of x by group
## (Holm)
## Col Mean-|
## Row Mean | May13A1 May13A2 May13A3 May13A4 May13A5 May13A6
## ---------+------------------------------------------------------------------
## May13A2 | 0.584662
## | 1.0000
## |
## May13A3 | 0.726734 0.286418
## | 1.0000 1.0000
## |
## May13A4 | 0.091249 -0.321520 -0.473906
## | 1.0000 1.0000 1.0000
## |
## May13A5 | 2.565797 2.549703 1.881309 1.901388
## | 0.9832 1.0000 1.0000 1.0000
## |
## May13A6 | -0.635001 -1.493788 -1.459668 -0.576956 -3.509345
## | 1.0000 1.0000 1.0000 1.0000 0.0492
## |
## May13A7 | -0.212107 -0.915996 -0.999219 -0.257499 -2.981008 0.453441
## | 1.0000 1.0000 1.0000 1.0000 0.2950 1.0000
## |
## Nov12A1 | -0.961309 -1.656760 -1.654111 -0.864001 -3.360207 -0.463510
## | 1.0000 1.0000 1.0000 1.0000 0.0837 1.0000
## |
## Nov12A2 | -1.842857 -3.135111 -2.736966 -1.471887 -4.875796 -1.352787
## | 1.0000 0.1795 0.6179 1.0000 0.0001* 1.0000
## |
## Nov12A3 | 0.559876 0.109546 -0.132979 0.355495 -1.913820 1.227666
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A4 | 1.575827 1.381371 1.199344 1.392178 0.229206 1.939483
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A5 | 2.538090 2.505617 1.862533 1.892225 0.010486 3.457040
## | 1.0000 1.0000 1.0000 1.0000 0.4958 0.0593
## |
## Nov12A6 | 0.990507 0.602230 0.250369 0.669691 -1.684325 1.781536
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A7 | 0.479086 -0.013029 -0.248225 0.282398 -2.116187 1.178097
## | 1.0000 0.9896 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A1 | -0.290758 -0.879093 -0.978572 -0.321741 -2.703183 0.273202
## | 1.0000 1.0000 1.0000 1.0000 0.6765 1.0000
## |
## Nov13A2 | -0.901542 -1.921244 -1.774427 -0.768143 -3.925721 -0.272921
## | 1.0000 1.0000 1.0000 1.0000 0.0099* 1.0000
## |
## Nov13A3 | 0.472943 0.153515 -0.015539 0.345925 -1.256781 0.908320
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A4 | -0.028629 -0.495052 -0.637647 -0.105444 -2.173051 0.488869
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A5 | 2.011625 1.859094 1.328184 1.485929 -0.512227 2.864077
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.4230
## |
## Nov13A6 | 0.051337 -0.589905 -0.732814 -0.056180 -2.723833 0.753462
## | 1.0000 1.0000 1.0000 1.0000 0.6394 1.0000
## |
## Nov13A7 | -0.252525 -0.823225 -0.930546 -0.291156 -2.632133 0.306804
## | 1.0000 1.0000 1.0000 1.0000 0.8208 1.0000
## Col Mean-|
## Row Mean | May13A7 Nov12A1 Nov12A2 Nov12A3 Nov12A4 Nov12A5
## ---------+------------------------------------------------------------------
## Nov12A1 | -0.824279
## | 1.0000
## |
## Nov12A2 | -1.771440 -0.602817
## | 1.0000 1.0000
## |
## Nov12A3 | 0.802661 1.460237 2.410822
## | 1.0000 1.0000 1.0000
## |
## Nov12A4 | 1.715894 2.088801 2.539967 1.252970
## | 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A5 | 2.941147 3.327080 4.792649 1.897044 -0.222797
## | 0.3335 0.0936 0.0002* 1.0000 1.0000
## |
## Nov12A6 | 1.294369 1.917652 3.120454 0.372768 -1.080997 -1.667378
## | 1.0000 1.0000 0.1876 1.0000 1.0000 1.0000
## |
## Nov12A7 | 0.730511 1.418952 2.432055 -0.103200 -1.326539 -2.094203
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A1 | -0.110971 0.639163 1.377873 -0.811862 -1.710971 -2.677596
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.7222
## |
## Nov13A2 | -0.739266 0.263578 1.151267 -1.507887 -2.076913 -3.859649
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.0129*
## |
## Nov13A3 | 0.628634 1.142965 1.679593 0.075624 -1.044527 -1.255110
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A4 | 0.145151 0.800077 1.459213 -0.503953 -1.506761 -2.159038
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A5 | 2.371322 2.834606 4.163719 1.390749 -0.495215 -0.514777
## | 1.0000 0.4612 0.0037* 1.0000 1.0000 1.0000
## |
## Nov13A6 | 0.286754 1.071132 2.093771 -0.550548 -1.582295 -2.687889
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.7042
## |
## Nov13A7 | -0.072712 0.664637 1.395117 -0.768460 -1.684315 -2.608222
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.8751
## Col Mean-|
## Row Mean | Nov12A6 Nov12A7 Nov13A1 Nov13A2 Nov13A3 Nov13A4
## ---------+------------------------------------------------------------------
## Nov12A7 | -0.503561
## | 1.0000
## |
## Nov13A1 | -1.230007 -0.744733
## | 1.0000 1.0000
## |
## Nov13A2 | -2.126875 -1.476067 -0.503799
## | 1.0000 1.0000 1.0000
## |
## Nov13A3 | -0.178953 0.149724 0.665458 1.076420
## | 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A4 | -0.852245 -0.431033 0.221992 0.695320 -0.454502
## | 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A5 | 1.119330 1.562189 2.183092 3.234149 0.903539 1.721014
## | 1.0000 1.0000 1.0000 0.1288 1.0000 1.0000
## |
## Nov13A6 | -1.021316 -0.464913 0.358085 1.057670 -0.455203 0.072469
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A7 | -1.177131 -0.700095 0.032818 0.534827 -0.636511 -0.190164
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## Col Mean-|
## Row Mean | Nov13A5 Nov13A6
## ---------+----------------------
## Nov13A6 | -2.117136
## | 1.0000
## |
## Nov13A7 | -2.120671 -0.316155
## | 1.0000 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha/2
| May13A1 | May13A2 | May13A3 | May13A4 | May13A5 | May13A6 | May13A7 | Nov12A1 | Nov12A2 | Nov12A3 | Nov12A4 | Nov12A5 | Nov12A6 | Nov12A7 | Nov13A1 | Nov13A2 | Nov13A3 | Nov13A4 | Nov13A5 | Nov13A6 | Nov13A7 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| May13A1 | |||||||||||||||||||||
| May13A2 | 1.00000 | ||||||||||||||||||||
| May13A3 | 1.00000 | 1.00000 | |||||||||||||||||||
| May13A4 | 1.00000 | 1.00000 | 1.00000 | ||||||||||||||||||
| May13A5 | 0.98324 | 1.00000 | 1.00000 | 1.00000 | |||||||||||||||||
| May13A6 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.04921 | ||||||||||||||||
| May13A7 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.29501 | 1.00000 | |||||||||||||||
| Nov12A1 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.08366 | 1.00000 | 1.00000 | ||||||||||||||
| Nov12A2 | 1.00000 | 0.1795 | 0.61785 | 1.00000 | 0.00014 | 1.00000 | 1.00000 | 1.00000 | |||||||||||||
| Nov12A3 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||||||||||
| Nov12A4 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | |||||||||||
| Nov12A5 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.49582 | 0.05932 | 0.3335 | 0.09361 | 0.00021 | 1.00000 | 1.00000 | ||||||||||
| Nov12A6 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.18759 | 1.00000 | 1.00000 | 1.00000 | |||||||||
| Nov12A7 | 1.00000 | 0.98961 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||||||
| Nov13A1 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.67647 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.72216 | 1.00000 | 1.00000 | |||||||
| Nov13A2 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.0099 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.01286 | 1.00000 | 1.00000 | 1.00000 | ||||||
| Nov13A3 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | |||||
| Nov13A4 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||
| Nov13A5 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.42302 | 1.00000 | 0.46118 | 0.00369 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.1288 | 1.00000 | 1.00000 | |||
| Nov13A6 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.63936 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.7042 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||
| Nov13A7 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.82079 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.8751 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 |
Depth data were not normally distributed and hence the difference in depth could only be analyzed using non-parametric Kruskal - Wallis rank sum test, and Conover - Iman post hoc test with Holm-Bonferroni correction.
Of the 210 survey - area comparisons, depth distributions were significantly different between areas as follows:
May13A5 - Nov12A2
May13A5 - Nov13A2
Nov12A2 - Nov12A5
Nov12A5 - Nov13A2
Nov12A2 - Nov13A5
Which corresponds with the plot of depth-distributions by survey and area above.
| id | maxhrs | minhrs | meanhrs |
|---|---|---|---|
| 1 | 46.417 | 11.500 | 20.48641 |
| 2 | 44.500 | 7.917 | 16.30586 |
| 3 | 24.250 | 8.167 | 16.55437 |
| 4 | 27.033 | 13.033 | 16.11076 |
| 5 | 40.200 | 11.667 | 17.79179 |
| 6 | 30.083 | 8.000 | 19.00436 |
| 7 | 31.866 | 10.417 | 19.47891 |
## ss id fhrs
## Min. :2.013e+09 Min. :1.000 Min. : 7.917
## 1st Qu.:2.013e+09 1st Qu.:2.000 1st Qu.:15.404
## Median :2.013e+09 Median :4.000 Median :16.133
## Mean :2.013e+09 Mean :3.909 Mean :17.905
## 3rd Qu.:2.013e+09 3rd Qu.:6.000 3rd Qu.:18.637
## Max. :2.013e+09 Max. :7.000 Max. :46.417
Boxplot of median with mean values plotted as red circle. The average trap soak-time (fishing hours) across all surveys and areas was 17.984 hrs with a median of 16.15, but the November 2012 survey had more varied and higher soak times than the May 2013 and Nov. 2013.
##
## Shapiro-Wilk normality test
##
## data: c3$Fhrs
## W = 0.66295, p-value < 2.2e-16
##
## Kruskal-Wallis rank sum test
##
## data: c3$Fhrs and c3$sa
## Kruskal-Wallis chi-squared = 233.68, df = 20, p-value < 2.2e-16
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 233.6845, df = 20, p-value = 0
##
##
## Comparison of x by group
## (Holm)
## Col Mean-|
## Row Mean | May13A1 May13A2 May13A3 May13A4 May13A5 May13A6
## ---------+------------------------------------------------------------------
## May13A2 | 0.019167
## | 1.0000
## |
## May13A3 | -1.845687 -2.286216
## | 1.0000 1.0000
## |
## May13A4 | -0.320485 -0.371906 1.113524
## | 1.0000 1.0000 1.0000
## |
## May13A5 | -2.246923 -2.789417 -0.397848 -1.419865
## | 1.0000 0.3043 1.0000 1.0000
## |
## May13A6 | -4.244256 -5.458261 -2.324296 -2.870062 -1.914471
## | 0.0019* 0.0000* 0.9901 0.2478 1.0000
## |
## May13A7 | -3.405686 -4.305897 -1.517177 -2.273520 -1.112647 0.802631
## | 0.0466 0.0015* 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A1 | -4.829058 -5.696259 -3.221177 -3.597107 -2.882630 -1.363272
## | 0.0001* 0.0000* 0.0858 0.0237* 0.2424 1.0000
## |
## Nov12A2 | -4.705998 -6.190516 -2.735283 -3.160669 -2.314961 -0.360371
## | 0.0002* 0.0000* 0.3489 0.1013 1.0000 1.0000
## |
## Nov12A3 | -3.182602 -3.831827 -1.463318 -2.215575 -1.096949 0.613429
## | 0.0963 0.0101* 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A4 | 1.010214 1.052566 2.001281 1.132860 2.211071 3.181677
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.0959
## |
## Nov12A5 | -5.773651 -7.082175 -4.041039 -4.190380 -3.676878 -2.060333
## | 0.0000* 0.0000* 0.0045* 0.0024* 0.0179* 1.0000
## |
## Nov12A6 | -8.907780 -11.11697 -7.200759 -6.538527 -6.849655 -5.420221
## | 0.0000* 0.0000* 0.0000* 0.0000* 0.0000* 0.0000*
## |
## Nov12A7 | -8.795131 -10.77815 -7.138466 -6.552716 -6.797659 -5.404561
## | 0.0000* 0.0000* 0.0000* 0.0000* 0.0000* 0.0000*
## |
## Nov13A1 | -2.040583 -2.429030 -0.353216 -1.333735 0.007911 1.720167
## | 1.0000 0.7858 1.0000 1.0000 0.9937 1.0000
## |
## Nov13A2 | 0.181700 0.215731 2.329854 0.482877 2.804046 5.276055
## | 1.0000 1.0000 0.9858 1.0000 0.2936 0.0000*
## |
## Nov13A3 | 1.274552 1.372309 2.543103 1.368215 2.814084 4.099715
## | 1.0000 1.0000 0.5833 1.0000 0.2872 0.0035*
## |
## Nov13A4 | -0.556590 -0.648738 0.979374 -0.177731 1.308371 2.876766
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.2448
## |
## Nov13A5 | 0.531164 0.627791 2.406335 0.738726 2.815455 4.874793
## | 1.0000 1.0000 0.8277 1.0000 0.2885 0.0001*
## |
## Nov13A6 | -1.353988 -1.730259 0.602853 -0.699303 1.027721 3.131292
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.1109
## |
## Nov13A7 | -3.296405 -3.893848 -1.656560 -2.358403 -1.308265 0.302434
## | 0.0671 0.0080* 1.0000 0.9230 1.0000 1.0000
## Col Mean-|
## Row Mean | May13A7 Nov12A1 Nov12A2 Nov12A3 Nov12A4 Nov12A5
## ---------+------------------------------------------------------------------
## Nov12A1 | -1.988652
## | 1.0000
## |
## Nov12A2 | -1.181754 1.114595
## | 1.0000 1.0000
## |
## Nov12A3 | -0.096705 1.745926 0.937774
## | 1.0000 1.0000 1.0000
## |
## Nov12A4 | 2.787816 3.743203 3.364131 2.761979
## | 0.3031 0.0140* 0.0536 0.3249
## |
## Nov12A5 | -2.728342 -0.452885 -1.811352 -2.376609 -4.110043
## | 0.3530 1.0000 1.0000 0.8880 0.0034*
## |
## Nov12A6 | -6.003656 -3.168143 -5.294800 -5.296309 -5.682125 -2.983793
## | 0.0000* 0.0996 0.0000* 0.0000* 0.0000* 0.1772
## |
## Nov12A7 | -5.972333 -3.252960 -5.275400 -5.311863 -5.729726 -3.073133
## | 0.0000* 0.0775 0.0000* 0.0000* 0.0000* 0.1335
## |
## Nov13A1 | 1.009175 2.667754 2.062936 1.012405 -2.146061 3.349600
## | 1.0000 0.4147 1.0000 1.0000 1.0000 0.0561
## |
## Nov13A2 | 4.217915 5.604789 5.923402 3.807271 -0.959949 6.869511
## | 0.0022* 0.0000* 0.0000* 0.0110* 1.0000 0.0000*
## |
## Nov13A3 | 3.573301 4.679637 4.359899 3.480038 -0.005114 5.223697
## | 0.0255 0.0003* 0.0012* 0.0358 0.4980 0.0000*
## |
## Nov13A4 | 2.228958 3.627888 3.197334 2.159691 -1.293095 4.282409
## | 1.0000 0.0214* 0.0923 1.0000 1.0000 0.0017*
## |
## Nov13A5 | 4.008306 5.356950 5.365326 3.724214 -0.726343 6.364118
## | 0.0051* 0.0000* 0.0000* 0.0150* 1.0000 0.0000*
## |
## Nov13A6 | 2.243440 3.899681 3.610500 2.101251 -1.727459 4.853401
## | 1.0000 0.0079* 0.0227* 1.0000 1.0000 0.0001*
## |
## Nov13A7 | -0.363487 1.437192 0.598651 -0.250049 -2.868556 2.007320
## | 1.0000 1.0000 1.0000 1.0000 0.2469 1.0000
## Col Mean-|
## Row Mean | Nov12A6 Nov12A7 Nov13A1 Nov13A2 Nov13A3 Nov13A4
## ---------+------------------------------------------------------------------
## Nov12A7 | -0.201945
## | 1.0000
## |
## Nov13A1 | 6.192118 6.185973
## | 0.0000* 0.0000*
## |
## Nov13A2 | 10.63522 10.36729 2.474252
## | 0.0000* 0.0000* 0.7006
## |
## Nov13A3 | 7.269017 7.285218 2.686030 1.240317
## | 0.0000* 0.0000* 0.3967 1.0000
## |
## Nov13A4 | 6.820619 6.818165 1.217743 -0.759148 -1.583188
## | 0.0000* 0.0000* 1.0000 1.0000 1.0000
## |
## Nov13A5 | 9.550708 9.411957 2.554888 0.429503 -0.915660 1.006743
## | 0.0000* 0.0000* 0.5695 1.0000 1.0000 1.0000
## |
## Nov13A6 | 8.237562 8.115501 0.915365 -1.801935 -2.213287 -0.530390
## | 0.0000* 0.0000* 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A7 | 4.769422 4.807396 -1.214680 -3.878340 -3.584495 -2.309030
## | 0.0002* 0.0002* 1.0000 0.0085* 0.0247* 1.0000
## Col Mean-|
## Row Mean | Nov13A5 Nov13A6
## ---------+----------------------
## Nov13A6 | -1.939976
## | 1.0000
## |
## Nov13A7 | -3.813158 -2.267140
## | 0.0108* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha/2
| May13A1 | May13A2 | May13A3 | May13A4 | May13A5 | May13A6 | May13A7 | Nov12A1 | Nov12A2 | Nov12A3 | Nov12A4 | Nov12A5 | Nov12A6 | Nov12A7 | Nov13A1 | Nov13A2 | Nov13A3 | Nov13A4 | Nov13A5 | Nov13A6 | Nov13A7 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| May13A1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| May13A2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| May13A3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| May13A4 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| May13A5 | 1 | 0.304318257715511 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| May13A6 | 0.00194605627325626 | 6.0153516764845e-06 | 0.99014924726454 | 0.247846643571116 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| May13A7 | 0.0465786037953744 | 0.00150608523631254 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov12A1 | 0.000139401665750064 | 1.65841091739907e-06 | 0.085762577046115 | 0.0237070897308893 | 0.242364685182563 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov12A2 | 0.000246854236398083 | 9.85544426575519e-08 | 0.348903164449488 | 0.10128218484263 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov12A3 | 0.0963172984556005 | 0.01011222165344 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov12A4 | 1 | 1 | 1 | 1 | 1 | 0.0958539432717497 | 0.303077471568711 | 0.0140370471898213 | 0.0536425536268057 | 0.324874306066398 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov12A5 | 1.08576347948883e-06 | 3.60740229365083e-10 | 0.00446641785383584 | 0.00242370829284479 | 0.017886611574173 | 1 | 0.352976745386185 | 1 | 1 | 0.887992322275862 | 0.00338945629930982 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov12A6 | 5.2359286286373e-16 | 2.1540429044105e-24 | 1.63692763192675e-10 | 1.18467837523924e-08 | 1.66133967069069e-09 | 7.33635323109579e-06 | 2.94496149948804e-07 | 0.0995555306130913 | 1.37718820396049e-05 | 1.37441209615493e-05 | 1.78443719959448e-06 | 0.177195831523622 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov12A7 | 1.28323842907066e-15 | 5.05069713249247e-23 | 2.48314927654109e-10 | 1.08942395287012e-08 | 2.29341629103919e-09 | 7.93138710985162e-06 | 3.51537134476469e-07 | 0.0774601726513087 | 1.50645764740843e-05 | 1.27400551234501e-05 | 1.38274101388467e-06 | 0.13349042546484 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov13A1 | 1 | 0.785810264447672 | 1 | 1 | 0.993689758253955 | 1 | 1 | 0.414747618618474 | 1 | 1 | 1 | 0.0560583239365252 | 9.81378305516571e-08 | 1.00729048552809e-07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov13A2 | 1 | 1 | 0.985768196167901 | 1 | 0.293604450979064 | 1.51029235971449e-05 | 0.00216693043064203 | 2.72354358587187e-06 | 4.64196786421573e-07 | 0.0109925087133428 | 1 | 1.46686440652933e-09 | 1.86845271596954e-22 | 2.10885855166046e-21 | 0.700634336679504 | 0 | 0 | 0 | 0 | 0 | 0 |
| Nov13A3 | 1 | 1 | 0.583274682919207 | 1 | 0.287217829755948 | 0.00351701983439009 | 0.025546838872539 | 0.000277886368368139 | 0.00119407105254703 | 0.0358253163893824 | 0.497960567181018 | 1.96014357639613e-05 | 1.03279080058057e-10 | 9.2889817131846e-11 | 0.396675142409312 | 1 | 0 | 0 | 0 | 0 | 0 |
| Nov13A4 | 1 | 1 | 1 | 1 | 1 | 0.244781715563496 | 1 | 0.021410245703009 | 0.0923298516429121 | 1 | 1 | 0.00165873956582547 | 1.99665065351593e-09 | 2.0182259660534e-09 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
| Nov13A5 | 1 | 1 | 0.827706966144769 | 1 | 0.288518841586274 | 0.000112949580366922 | 0.0050814730983063 | 1.00998716838306e-05 | 9.71726948443586e-06 | 0.0150064118947393 | 1 | 3.47698121001712e-08 | 2.61679204699592e-18 | 8.38112350192771e-18 | 0.569510613425275 | 1 | 1 | 1 | 0 | 0 | 0 |
| Nov13A6 | 1 | 1 | 1 | 1 | 1 | 0.110904558548165 | 1 | 0.00786507176793555 | 0.0227032041040827 | 1 | 1 | 0.00012461267792295 | 9.70357762936814e-14 | 2.41915146938145e-13 | 1 | 1 | 1 | 1 | 1 | 0 | 0 |
| Nov13A7 | 0.0671026136595497 | 0.00799786259994307 | 1 | 0.923049974115501 | 1 | 1 | 1 | 1 | 1 | 1 | 0.246886570042301 | 1 | 0.000183565624161181 | 0.000153855187385531 | 1 | 0.00845489896960382 | 0.0246776078168709 | 1 | 0.0108145580268575 | 1 | 0 |
Trap soak times:
Of the 210 area- survey combinations 69 were significantly different (Kruskal Wallis rank sum test with Conover-Iman post-hoc test w Holm correction, p<0.05):
May13A1 - May13A6
May13A1 - Nov12A1
May13A1 - Nov12A2
May13A1 - Nov12A5
May13A1 - Nov12A6
May13A1 - Nov12A7
May13A2 - May13A6
May13A2 - May13A7
May13A2 - Nov12A1
May13A2 - Nov12A2
May13A2 - Nov12A5
May13A2 - Nov12A6
May13A2 - Nov12A7
May13A3 - Nov12A6
May13A3 - Nov12A7
May13A4 - Nov12A1
May13A4 - Nov12A5
May13A4 - Nov12A6
May13A4 - Nov12A7
May13A5 - Nov12A5
May13A5 - Nov12A6
May13A5 - Nov12A7
May13A6 - Nov12A6
May13A6 - Nov12A7
May13A6 - Nov13A2
May13A6 - Nov13A3
May13A6 - Nov13A5
May13A7 - Nov12A6
May13A7 - Nov12A7
May13A7 - Nov13A2
May13A7 - Nov13A5
Nov12A1 - Nov12A4
Nov12A1 - Nov13A2
Nov12A1 - Nov13A3
Nov12A1 - Nov13A4
Nov12A1 - Nov13A5
Nov12A1 - Nov13A6
Nov12A2 - Nov12A6
Nov12A2 - Nov12A7
Nov12A2 - Nov13A2
Nov12A2 - Nov13A3
Nov12A2 - Nov13A5
Nov12A3 - Nov12A6
Nov12A3 - Nov12A7
Nov12A3 - Nov13A5
Nov12A4 - Nov12A5
Nov12A4 - Nov12A6
Nov12A4 - Nov12A7
Nov12A5 - Nov13A2
Nov12A5 - Nov13A3
Nov12A5 - Nov13A4
Nov12A5 - Nov13A5
Nov12A5 - Nov13A6
Nov12A6 - Nov13A1
Nov12A6 - Nov13A2
Nov12A6 - Nov13A3
Nov12A6 - Nov13A4
Nov12A6 - Nov13A5
Nov12A6 - Nov13A6
Nov12A6 - Nov13A7
Nov12A7 - Nov13A1
Nov12A7 - Nov13A2
Nov12A7 - Nov13A3
Nov12A7 - Nov13A4
Nov12A7 - Nov13A5
Nov12A7 - Nov13A6
Nov12A7 - Nov13A7
Nov13A1 - Nov13A7
Nov13A5 - Nov13A7
eys.
Fishing hours for traps was significantly affected by areas (except area 3 which had a very large variability), however, the number of gillnet sets was very low pr area and survey.
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 29.8951, df = 17, p-value = 0.03
##
##
## Comparison of x by group
## (Holm)
## Col Mean-|
## Row Mean | May13A1 May13A2 May13A3 May13A4 May13A5 May13A6
## ---------+------------------------------------------------------------------
## May13A2 | -2.352198
## | 1.0000
## |
## May13A3 | 0.000000 2.352198
## | 1.0000 1.0000
## |
## May13A4 | -2.042245 0.793793 -2.042245
## | 1.0000 1.0000 1.0000
## |
## May13A5 | -0.542814 3.133943 -0.542814 2.824972
## | 1.0000 0.2265 1.0000 0.5123
## |
## May13A6 | -0.770026 1.952546 -0.770026 1.547694 -0.416492
## | 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A2 | -0.664463 2.477687 -0.664463 2.088271 -0.236387 0.192611
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A3 | -1.130276 0.893018 -1.130276 0.518181 -0.916365 -0.535103
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A5 | -1.193798 1.851459 -1.193798 1.364447 -1.103250 -0.452204
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A6 | -0.632954 1.535057 -0.632954 1.188769 -0.274325 0.039153
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov12A7 | -0.915523 2.816487 -0.915523 2.468401 -0.660311 -0.035383
## | 1.0000 0.5204 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A1 | 0.000000 3.841123 0.000000 3.620514 0.886413 1.088982
## | 1.0000 0.0285 1.0000 0.0555 1.0000 1.0000
## |
## Nov13A2 | -0.665945 3.067396 -0.665945 2.754895 -0.205900 0.274077
## | 1.0000 0.2691 1.0000 0.6036 1.0000 1.0000
## |
## Nov13A3 | 0.000000 3.079750 0.000000 2.765213 0.710711 0.943086
## | 1.0000 0.2617 1.0000 0.5916 1.0000 1.0000
## |
## Nov13A4 | -0.750591 2.590137 -0.750591 2.202029 -0.364573 0.119972
## | 1.0000 0.9093 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A5 | -1.974934 0.405652 -1.974934 -0.210445 -2.308422 -1.465016
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A6 | -1.487848 0.901763 -1.487848 0.412718 -1.467275 -0.879148
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A7 | -1.866336 0.347713 -1.866336 -0.185723 -2.021325 -1.342699
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## Col Mean-|
## Row Mean | Nov12A2 Nov12A3 Nov12A5 Nov12A6 Nov12A7 Nov13A1
## ---------+------------------------------------------------------------------
## Nov12A3 | -0.719835
## | 1.0000
## |
## Nov12A5 | -0.742966 0.235900
## | 1.0000 1.0000
## |
## Nov12A6 | -0.110743 0.497321 0.393167
## | 1.0000 1.0000 1.0000
## |
## Nov12A7 | -0.301036 0.591511 0.593834 -0.071584
## | 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A1 | 1.004574 1.429698 1.887560 0.800631 1.585733
## | 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A2 | 0.067816 0.813933 0.953379 0.162786 0.466161 -1.114339
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A3 | 0.840487 1.305130 1.541187 0.730872 1.228303 0.000000
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.5000
## |
## Nov13A4 | -0.094178 0.679106 0.700771 0.050039 0.215346 -1.186789
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A5 | -1.853285 -0.590634 -1.238278 -1.199726 -1.934109 -2.985820
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.3345
## |
## Nov13A6 | -1.155670 -0.182718 -0.562948 -0.756975 -1.076661 -2.104135
## | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A7 | -1.663465 -0.561206 -1.098211 -1.135463 -1.663012 -2.639398
## | 1.0000 1.0000 1.0000 1.0000 1.0000 0.8080
## Col Mean-|
## Row Mean | Nov13A2 Nov13A3 Nov13A4 Nov13A5 Nov13A6
## ---------+-------------------------------------------------------
## Nov13A3 | 0.880962
## | 1.0000
## |
## Nov13A4 | -0.185723 -0.969009
## | 1.0000 1.0000
## |
## Nov13A5 | -2.207805 -2.498116 -1.887066
## | 1.0000 1.0000 1.0000
## |
## Nov13A6 | -1.350809 -1.822234 -1.135125 0.501957
## | 1.0000 1.0000 1.0000 1.0000
## |
## Nov13A7 | -1.918541 -2.285785 -1.670388 -0.005836 -0.463550
## | 1.0000 1.0000 1.0000 1.0000 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha/2
| May13A1 | May13A2 | May13A3 | May13A4 | May13A5 | May13A6 | Nov12A2 | Nov12A3 | Nov12A5 | Nov12A6 | Nov12A7 | Nov13A1 | Nov13A2 | Nov13A3 | Nov13A4 | Nov13A5 | Nov13A6 | Nov13A7 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| May13A1 | ||||||||||||||||||
| May13A2 | 1.00000 | |||||||||||||||||
| May13A3 | 1.00000 | 1.00000 | ||||||||||||||||
| May13A4 | 1.00000 | 1.00000 | 1.00000 | |||||||||||||||
| May13A5 | 1.00000 | 0.22653 | 1.00000 | 0.51231 | ||||||||||||||
| May13A6 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | |||||||||||||
| Nov12A2 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||||||||||
| Nov12A3 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | |||||||||||
| Nov12A5 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||||||||
| Nov12A6 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | |||||||||
| Nov12A7 | 1.00000 | 0.52039 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||||||
| Nov13A1 | 1.00000 | 0.02855 | 1.00000 | 0.05548 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | |||||||
| Nov13A2 | 1.00000 | 0.26906 | 1.00000 | 0.60359 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||||
| Nov13A3 | 1.00000 | 0.26174 | 1.00000 | 0.59158 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.5 | 1.00000 | |||||
| Nov13A4 | 1.00000 | 0.9093 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||||
| Nov13A5 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.3345 | 1.00000 | 1.00000 | 1.00000 | |||
| Nov13A6 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | ||
| Nov13A7 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 0.80797 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 |
Gill net fishing time:
K-W test significant, but only 2 were significant (Conover-Iman post-hoc test w Holm correction, p<0.05):
Nov13A1 - May13A2
Nov13A1 - May13A5
Must aggregate data per station
Shapiro test for normality and Q-Q plots shows that data is non-normal and zero-inflated.
##
## Shapiro-Wilk normality test
##
## data: cpue.tr.st$CPUEw
## W = 0.55152, p-value < 2.2e-16
### Test for difference in CPUEw of traps between areas (non-parametric test)
CPUE data are non-normal (see Shapiro-test & Q-Q plots above) & zero-inflated (many stations with 0 catch).
Zero-inflated GAM model A GAM model approach is more appropriate than a non-parametric approach (e.g Kruskal Wallis) as it evaluates the effects of all factors at the same time, instead of evaluating the effects of area separate from survey.
Developed seperate trap and gillnet models for CPUE (weight and numbers) including depth, area and survey.
Tested models where survey was included as a random factor, although this excludes evaluation of the potential significant effects of the surveys on the results.
Tested wether specifying the nu-function improved the fit (reduced the AIC)
Use Zero-inflated distributions to fit the model (available in the GAMLSS package)
Statistical analysis of plot of trap catch rates.
Because traps and gillnets are very different & their deployment is confounded with depth (gillnets fish at surface, traps fishat bottom) it is not appropriate to combine both gear into a single model, they must be evaluated seperately.
## GAMLSS-RS iteration 1: Global Deviance = 681.6152
## GAMLSS-RS iteration 2: Global Deviance = 681.6145
## GAMLSS-RS iteration 1: Global Deviance = 681.3479
## GAMLSS-RS iteration 2: Global Deviance = 681.3479
## GAMLSS-RS iteration 1: Global Deviance = 396.3758
## GAMLSS-RS iteration 2: Global Deviance = 396.3741
## GAMLSS-RS iteration 3: Global Deviance = 396.3741
## GAMLSS-RS iteration 1: Global Deviance = 395.0602
## GAMLSS-RS iteration 2: Global Deviance = 395.0599
## GAMLSS-RS iteration 1: Global Deviance = 640.1004
## GAMLSS-RS iteration 2: Global Deviance = 640.1004
## GAMLSS-RS iteration 1: Global Deviance = 358.2109
## GAMLSS-RS iteration 2: Global Deviance = 358.2106
## df AIC
## mod555 21.00000 400.2106
## mod55 12.00000 419.0599
## mod44 11.82553 420.0252
## mod333 21.00000 682.1004
## mod33 12.00000 705.3479
## mod22 12.66640 706.9473
## ******************************************************************
## Family: c("ZAGA", "Zero Adjusted GA")
##
## Call: gamlss(formula = CPUEn ~ depth + factor(id) + factor(survey),
## nu.formula = ~depth + factor(id) + factor(survey),
## family = ZAGA, data = c22)
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1133836 0.1931986 -10.939 < 2e-16 ***
## depth -0.0009092 0.0025924 -0.351 0.72592
## factor(id)2 0.2896902 0.1674014 1.731 0.08401 .
## factor(id)3 0.1802016 0.2141091 0.842 0.40030
## factor(id)4 -0.0014326 0.2581430 -0.006 0.99557
## factor(id)5 0.5108807 0.2032557 2.513 0.01220 *
## factor(id)6 -0.2143588 0.1778905 -1.205 0.22864
## factor(id)7 0.1928098 0.1960893 0.983 0.32584
## factor(survey)2013002 0.3019887 0.1084315 2.785 0.00551 **
## factor(survey)2013005 0.2914302 0.1371937 2.124 0.03403 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Sigma link function: log
## Sigma Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2072 0.0367 -5.645 2.47e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Nu link function: logit
## Nu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0815597 0.3066923 -0.266 0.7904
## depth -0.0006377 0.0043131 -0.148 0.8825
## factor(id)2 -0.0416457 0.2810371 -0.148 0.8822
## factor(id)3 0.2110005 0.3472408 0.608 0.5436
## factor(id)4 -0.1382635 0.4287207 -0.323 0.7472
## factor(id)5 0.2580592 0.3275442 0.788 0.4311
## factor(id)6 -0.5981704 0.3069647 -1.949 0.0518 .
## factor(id)7 0.1570886 0.3217318 0.488 0.6255
## factor(survey)2013002 0.0848371 0.1881020 0.451 0.6521
## factor(survey)2013005 0.9733749 0.2129305 4.571 5.8e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## No. of observations in the fit: 670
## Degrees of Freedom for the fit: 21
## Residual Deg. of Freedom: 649
## at cycle: 2
##
## Global Deviance: 358.2106
## AIC: 400.2106
## SBC: 494.8635
## ******************************************************************
## Single term deletions for
## mu
##
## Model:
## CPUEn ~ depth + factor(id) + factor(survey)
## Df AIC LRT Pr(Chi)
## <none> 400.21
## depth 1 398.34 0.1247 0.7239712
## factor(id) 6 410.96 22.7474 0.0008856 ***
## factor(survey) 2 404.46 8.2476 0.0161830 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## ******************************************************************
## Summary of the Randomised Quantile Residuals
## mean = 0.00369002
## variance = 0.9940998
## coef. of skewness = 0.6099116
## coef. of kurtosis = 5.493112
## Filliben correlation coefficient = 0.9821562
## ******************************************************************
## ******************************************************************
## Family: c("ZAGA", "Zero Adjusted GA")
##
## Call: gamlss(formula = CPUEw ~ depth + factor(id) + factor(survey),
## nu.formula = ~depth + factor(id) + factor(survey),
## family = ZAGA, data = c22)
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.284504 0.235061 -5.465 6.62e-08 ***
## depth -0.001742 0.003227 -0.540 0.58946
## factor(id)2 0.265502 0.200255 1.326 0.18537
## factor(id)3 0.147263 0.250991 0.587 0.55759
## factor(id)4 -0.440717 0.315634 -1.396 0.16310
## factor(id)5 -0.195117 0.243270 -0.802 0.42281
## factor(id)6 -0.015500 0.207863 -0.075 0.94058
## factor(id)7 -0.057084 0.233182 -0.245 0.80669
## factor(survey)2013002 0.031849 0.131891 0.241 0.80926
## factor(survey)2013005 -0.498068 0.168559 -2.955 0.00324 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Sigma link function: log
## Sigma Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003268 0.035115 0.093 0.926
##
## ------------------------------------------------------------------
## Nu link function: logit
## Nu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.134996 0.306882 -0.440 0.660
## depth -0.003494 0.004343 -0.805 0.421
## factor(id)2 0.107987 0.280948 0.384 0.701
## factor(id)3 0.314591 0.346826 0.907 0.365
## factor(id)4 0.023579 0.429432 0.055 0.956
## factor(id)5 0.389988 0.327546 1.191 0.234
## factor(id)6 -0.471680 0.307445 -1.534 0.125
## factor(id)7 0.323361 0.321910 1.005 0.316
## factor(survey)2013002 0.024872 0.188524 0.132 0.895
## factor(survey)2013005 0.996838 0.213314 4.673 3.61e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## No. of observations in the fit: 670
## Degrees of Freedom for the fit: 21
## Residual Deg. of Freedom: 649
## at cycle: 2
##
## Global Deviance: 640.1004
## AIC: 682.1004
## SBC: 776.7532
## ******************************************************************
## Single term deletions for
## mu
##
## Model:
## CPUEw ~ depth + factor(id) + factor(survey)
## Df AIC LRT Pr(Chi)
## <none> 682.10
## depth 1 680.39 0.2879 0.591551
## factor(id) 6 680.18 10.0751 0.121526
## factor(survey) 2 689.27 11.1739 0.003747 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## ******************************************************************
## Summary of the Randomised Quantile Residuals
## mean = 0.01402976
## variance = 0.957426
## coef. of skewness = 0.2014206
## coef. of kurtosis = 3.04717
## Filliben correlation coefficient = 0.9979663
## ******************************************************************
Trap CPUE GAM models The CPUEn models had better fit than the CPUEw models, and models with nu-function specified had better fit than those without.
The overall bestfitting model was mod555 of CPUEn with survey as an ordniary factor both area and survey were significant variable (when excluding the smoothing function), but not depth. All survey and areas 1 & 5 were significant factors in the model
For the CPUEw model (mod333), only surveys was a significant varible, with only the Nov.2012 (intercept), and Nov.2013 survey being significant factors in the model.
Conclusion: difference in trap CPUE by surveys was significantly different, but not so between areas.
## GAMLSS-RS iteration 1: Global Deviance = 161.0973
## GAMLSS-RS iteration 2: Global Deviance = 161.0973
## GAMLSS-RS iteration 1: Global Deviance = 160.5285
## GAMLSS-RS iteration 2: Global Deviance = 160.5285
## GAMLSS-RS iteration 1: Global Deviance = 184.9555
## GAMLSS-RS iteration 2: Global Deviance = 184.9551
## GAMLSS-RS iteration 1: Global Deviance = 184.1489
## GAMLSS-RS iteration 2: Global Deviance = 184.1487
## GAMLSS-RS iteration 1: Global Deviance = 136.3068
## GAMLSS-RS iteration 2: Global Deviance = 136.3065
## GAMLSS-RS iteration 1: Global Deviance = 159.9272
## GAMLSS-RS iteration 2: Global Deviance = 159.9267
## df AIC
## mod333 21.00000 178.3065
## mod22 10.00001 181.0973
## mod33 12.00000 184.5285
## mod555 21.00000 201.9267
## mod44 10.00001 204.9552
## mod55 12.00000 208.1487
## ******************************************************************
## Family: c("ZAGA", "Zero Adjusted GA")
##
## Call: gamlss(formula = CPUEw ~ depth + factor(id) + factor(survey),
## nu.formula = ~depth + factor(id) + factor(survey),
## family = ZAGA, data = c222)
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.488227 0.645189 0.757 0.4533
## depth -0.006344 0.004267 -1.487 0.1444
## factor(id)2 -0.083590 0.552826 -0.151 0.8805
## factor(id)3 -1.386996 0.718770 -1.930 0.0603 .
## factor(id)4 -0.390282 0.580326 -0.673 0.5048
## factor(id)5 -0.291541 0.621620 -0.469 0.6414
## factor(id)6 -1.611851 0.702743 -2.294 0.0268 *
## factor(id)7 -0.756164 0.644943 -1.172 0.2475
## factor(survey)2013002 -0.010671 0.461668 -0.023 0.9817
## factor(survey)2013005 0.245101 0.431467 0.568 0.5729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Sigma link function: log
## Sigma Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01861 0.08747 -0.213 0.832
##
## ------------------------------------------------------------------
## Nu link function: logit
## Nu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.914184 272.559352 -0.036 0.971
## depth -0.008674 0.012620 -0.687 0.496
## factor(id)2 10.738405 272.558507 0.039 0.969
## factor(id)3 -1.835308 416.422793 -0.004 0.997
## factor(id)4 11.495711 272.558213 0.042 0.967
## factor(id)5 9.960912 272.558854 0.037 0.971
## factor(id)6 -1.789029 399.478786 -0.004 0.996
## factor(id)7 8.160657 272.561501 0.030 0.976
## factor(survey)2013002 -1.242747 1.071254 -1.160 0.252
## factor(survey)2013005 -13.337185 138.032693 -0.097 0.923
##
## ------------------------------------------------------------------
## No. of observations in the fit: 64
## Degrees of Freedom for the fit: 21
## Residual Deg. of Freedom: 43
## at cycle: 2
##
## Global Deviance: 136.3065
## AIC: 178.3065
## SBC: 223.643
## ******************************************************************
## Single term deletions for
## mu
##
## Model:
## CPUEw ~ depth + factor(id) + factor(survey)
## Df AIC LRT Pr(Chi)
## <none> 178.31
## depth 1 177.89 1.5827 0.2084
## factor(id) 6 175.33 9.0219 0.1723
## factor(survey) 2 174.88 0.5688 0.7525
## ******************************************************************
## Summary of the Randomised Quantile Residuals
## mean = 0.002960327
## variance = 1.046671
## coef. of skewness = -0.1705965
## coef. of kurtosis = 2.149268
## Filliben correlation coefficient = 0.9903634
## ******************************************************************
## ******************************************************************
## Family: c("ZAGA", "Zero Adjusted GA")
##
## Call: gamlss(formula = CPUEn ~ depth + factor(id) + factor(survey),
## nu.formula = ~depth + factor(id) + factor(survey),
## family = ZAGA, data = c222)
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.209808 0.640161 -0.328 0.745
## depth -0.016392 0.003818 -4.293 9.85e-05 ***
## factor(id)2 0.763394 0.591571 1.290 0.204
## factor(id)3 -0.610027 0.766685 -0.796 0.431
## factor(id)4 0.673012 0.578518 1.163 0.251
## factor(id)5 0.881939 0.619440 1.424 0.162
## factor(id)6 0.183003 0.714614 0.256 0.799
## factor(id)7 0.553867 0.634909 0.872 0.388
## factor(survey)2013002 0.110705 0.472581 0.234 0.816
## factor(survey)2013005 0.353101 0.425734 0.829 0.411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Sigma link function: log
## Sigma Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01585 0.08694 0.182 0.856
##
## ------------------------------------------------------------------
## Nu link function: logit
## Nu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.914184 272.883567 -0.036 0.971
## depth -0.008674 0.012620 -0.687 0.496
## factor(id)2 10.738405 272.882720 0.039 0.969
## factor(id)3 -1.835308 416.855152 -0.004 0.997
## factor(id)4 11.495711 272.882428 0.042 0.967
## factor(id)5 9.960912 272.883068 0.037 0.971
## factor(id)6 -1.789029 399.700064 -0.004 0.996
## factor(id)7 8.160657 272.885715 0.030 0.976
## factor(survey)2013002 -1.242747 1.071254 -1.160 0.252
## factor(survey)2013005 -13.337185 138.032692 -0.097 0.923
##
## ------------------------------------------------------------------
## No. of observations in the fit: 64
## Degrees of Freedom for the fit: 21
## Residual Deg. of Freedom: 43
## at cycle: 2
##
## Global Deviance: 159.9267
## AIC: 201.9267
## SBC: 247.2632
## ******************************************************************
## Single term deletions for
## mu
##
## Model:
## CPUEn ~ depth + factor(id) + factor(survey)
## Df AIC LRT Pr(Chi)
## <none> 201.93
## depth 1 209.09 9.1580 0.002476 **
## factor(id) 6 196.67 6.7395 0.345616
## factor(survey) 2 198.73 0.8065 0.668160
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## ******************************************************************
## Summary of the Randomised Quantile Residuals
## mean = 0.01991237
## variance = 0.9986423
## coef. of skewness = 0.01118196
## coef. of kurtosis = 2.542639
## Filliben correlation coefficient = 0.9922952
## ******************************************************************
Gillnet CPUE GAM models The gillnet GAM models had much lower AICs than the trap models, indicating a better overall model fit for gillnet data than for trap data.
All CPUEw models had lower AIC than the CPUEn model (in contrast to the traps GAM models)
The best fitting model was the CPUEw model with survey as an ordinary factor and the nu.function specified (mod333). Only area 3 & 6 were significant factors in the model, and in contrast to the traps surveys were not a significant factors.
Conclusion: there were no significant differences in CPUE (w) for gillnets between surveys, and only significant differences involving areas 3 and 6.
##
## Welch Two Sample t-test
##
## data: subset(cpue.st, gear == "TB")$depth and (subset(cpue.st, gear == "GN")$depth)
## t = 3.5683, df = 67.859, p-value = 0.0006648
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 6.034582 21.347788
## sample estimates:
## mean of x mean of y
## 31.73806 18.04688
Depth was, however, not a significant factor in any of the four models developed for the trap CPUE, by weight or numbers. The depth distribution of bottom depth at the trap and gillnet stations (Gillnet fishing depth was at the surface, while trap fishing depth = bottom depth) were significantly different (t-test, p<0.05, df=67.9 - see also frequncy plot above). Therefore, the models including both gear types led to a wider span and a more diverse distribution of the depths than for the traps-only data set.
Only for gillnets and traps and stations with catches (excluding no-catch)
Split CPUEw by depth ranges (0-30m and >30m) for the trap catches. Gillnets were all taken at the surface so belong in the upper depth range.
PCA analysis was carried out to explore the variability in the catch data (CPUE-by-weight) in relation to: - survey - gear - area - Trophic group
PCA was carried out on average catches (CPUE) per survey and area (needed to create a M*N matrix - cannot have uneven number of observations per group).
## PC1 PC2 PC3 PC4 PC5 gear
## GN_2012901_2 -0.1385169 -0.08034762 -0.08267280 0.022102618 5.431281e-05 GN
## GN_2012901_3 -0.1415117 -0.10512615 -0.08175626 -0.011103180 -8.979356e-05 GN
## GN_2012901_5 -0.1461118 0.12919315 -0.07520536 -0.007790960 3.048672e-04 GN
## GN_2012901_6 -0.1453167 0.08869074 -0.07633769 -0.008363482 2.366495e-04 GN
## GN_2012901_7 -0.1404506 -0.15917516 -0.08326732 -0.011867189 -1.808276e-04 GN
## GN_2013002_1 -0.1249389 0.14835990 -0.07432751 -0.009791310 3.346150e-04 GN
## survey area
## GN_2012901_2 2012901 2
## GN_2012901_3 2012901 3
## GN_2012901_5 2012901 5
## GN_2012901_6 2012901 6
## GN_2012901_7 2012901 7
## GN_2013002_1 2013002 1
## PC1 PC2 PC3 PC4 PC5
## Carni. 0.0196184927 -0.999315870 -0.02793805 -0.014125826 -0.0016831338
## Herb. 0.1054558856 -0.010605169 -0.04894918 0.993157135 0.0030771125
## Plankt. 0.9941742176 0.020538052 0.01635847 -0.104538066 -0.0000823814
## Invert. -0.0105679342 -0.028801239 0.99820062 0.050050201 -0.0122191691
## Coral. -0.0003387364 -0.001999746 0.01230312 -0.002477072 0.9999191885
## Trophic_Group
## Carni. Carni.
## Herb. Herb.
## Plankt. Plankt.
## Invert. Invert.
## Coral. Coral.
(gillnets were only at the surface)
## PC1 PC2 PC3 PC4
## TB_2012901_1_<30m 0.002617937 0.011362987 -0.096517255 -0.0010775816
## TB_2012901_1_>30m -0.057667184 0.034936698 -0.007360843 -0.0016139402
## TB_2012901_2_<30m 0.295125463 -0.066927684 -0.044668108 0.0026241585
## TB_2012901_2_>30m 0.102648502 -0.006624161 0.038833922 0.0007180965
## TB_2012901_3_<30m 0.139611391 -0.023114102 -0.042842006 0.0007227938
## TB_2012901_3_>30m 0.122198277 -0.028139788 -0.175948243 0.0002073881
## PC5 survey area depth
## TB_2012901_1_<30m 0.0007591324 2012901 1 <30m
## TB_2012901_1_>30m 0.0004591729 2012901 1 >30m
## TB_2012901_2_<30m 0.0023524390 2012901 2 <30m
## TB_2012901_2_>30m -0.0194403608 2012901 2 >30m
## TB_2012901_3_<30m 0.0015137099 2012901 3 <30m
## TB_2012901_3_>30m 0.0013818531 2012901 3 >30m
## PC1 PC2 PC3 PC4 PC5
## Carni. 0.708269047 -0.1474988955 0.6902738068 0.010244759 0.004018719
## Invert. 0.652272628 -0.2368360468 -0.7199983925 0.006358460 0.003315134
## Coral. 0.005160458 -0.0002843214 0.0002769255 0.012714451 -0.999905773
## Plankt. -0.269723456 -0.9602738107 0.0715348915 0.002253698 -0.001070507
## Herb. -0.010862927 0.0051855946 -0.0026587615 0.999843927 0.012655391
## Trophic_Group
## Carni. Carni.
## Invert. Invert.
## Coral. Coral.
## Plankt. Plankt.
## Herb. Herb.
In Figure 4 panels A and B clearly show how the catch composition differed between traps and gillnets, both in relation to trophic groups and main fish families caught. Carinvores were caught in similar quantities by both the traps and gillnets, coralivores only in traps (but in very low quantities) invertivores mainly in traps, planktivores and herbivores mainly in gillnets.
Catch composition by families (Figure 4 panel A and B) showed clear differences in the species selectivity of the gear types.
Catch composition of traps showed some differences by depth range (<30m or > 30m), for the plantivores trophic group and Acunthuridae and Carangidae families that were only caught at shallow depths, while no trophic groups or families were uniquely caught at depths greater than 30m.
PCA of catch weight and numbers (CPUEw and CPUEw) by trophic group gave similar results and showed how trap and gillnet catches differed. The following analysis therefore focused solely on PCA by CPUEw.
In the PCA analysis of both gillnet and traps (Figure 4, panel C and D). The first two principal components explained 88% of the variation. A large gillnet catch of Planktivores (56.69 kg gillnet catch of Naso hexacanthus in area 2 during the Nov. 2013 survey) drove the variability along PC1. PC2 was driven by high catches of carnivores during the November 2013 survey d. Catches of coralivores, invertivores and herbivores contributed little to the variability in either of the two first PCs (but these were caught in low quantities in just a few surveys and areas).
The PCA analysis of the trap catches (Figure 4, panel E and F) the first two PCs explained 67.9% of the variability with PC1 and PC2 contributing fairly equally (35.9% and 32% respectively). Catches of carnivores and invertivores drove the variability along PC1 while a 10.14kg catch of planktivores (Naso hexacanthus) in area 5 in May 2013 in shallow waters drove the variability along PC2. Apart from the May.2013 point no areas showed particular differences between deep and shallow areas.
Trap catches were dominated by herbivores, gillnet catches by carnivores, while gillnet catches in area 2 during the Nov. 2013 survey had very high catches of planktivores compared to other gears, surveys and areas. Trap catches did not show marked difference by depth category, except for Area 5 during the May 2013 survey where there were (relatively) large catches of planktivores.
However, this is still descriptive. Need to evaluate the differences statistically. Will use GAM model.
(This also allows to use data on station level, not just average catches per area/survey needed to construct a matrix necessary for PCA)
Expand on the PCA analysis and descriptive plots using GAM models. Compartive, evaluating the same variables as in the PCA analysis: - Survey - Area - Depth Category (shallow / deep) - Trophic group
Separate models for Gillnets and Traps,adding depth category as factor variable.
## GAMLSS-RS iteration 1: Global Deviance = -113.6846
## GAMLSS-RS iteration 2: Global Deviance = -113.6846
## minimum GAIC(k= 2 ) family: GIG
## minimum GAIC(k= 3.84 ) family: IG
## minimum GAIC(k= 5.83 ) family: IG
## GAIG with k= 5.83
## IG GIG LNO LOGNO LOGNO2 GG BCTo
## -538.4687 -534.3554 -530.4779 -530.4779 -530.4779 -525.5563 -519.6675
## GB2 IGAMMA PARETO2o PARETO2 GP EXP WEI3
## -507.5812 -505.0091 -492.4760 -492.4757 -492.4757 -489.1199 -485.4980
## WEI WEI2 GA BCPEo BCCGo BCCG exGAUS
## -485.4980 -485.4979 -483.3363 812.8877 18578.5953 NA NA
## BCT BCPE
## NA NA
## GAIG with k= 5.83
## GAMLSS-RS iteration 1: Global Deviance = -608.4287
## GAMLSS-RS iteration 2: Global Deviance = -608.4288
## GAIG with k= 5.83
## GAMLSS-RS iteration 1: Global Deviance = -608.4687
## GAMLSS-RS iteration 2: Global Deviance = -608.4688
## GAIG with k= 5.83
## GAMLSS-RS iteration 1: Global Deviance = -608.4287
## GAMLSS-RS iteration 2: Global Deviance = -608.4288
## GAIG with k= 5.83
## GAMLSS-RS iteration 1: Global Deviance = -608.1104
## GAMLSS-RS iteration 2: Global Deviance = -608.1105
## df AIC
## mod10 12.00000 -584.4288
## mod7 12.00002 -584.4287
## mod12 12.00000 -584.1105
## mod8 13.00000 -582.4688
## GAIG with k= 5.83
## ******************************************************************
## Family: c("IG", "Inverse Gaussian")
##
## Call: gamlss(formula = CPUEw ~ factor(Drng) + factor(id) +
## factor(TGShort), family = names(getOrder(t1, 3)[1]),
## data = na.omit(subset(c43, gear == "TB" & CPUEw > 0)))
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.63095 0.26401 -6.178 1.93e-09 ***
## factor(Drng)>30m -0.11392 0.17945 -0.635 0.5260
## factor(id)2 0.15922 0.30543 0.521 0.6025
## factor(id)3 0.40006 0.40240 0.994 0.3209
## factor(id)4 -0.85216 0.44562 -1.912 0.0567 .
## factor(id)5 -0.29039 0.33324 -0.871 0.3842
## factor(id)6 -0.03484 0.29693 -0.117 0.9067
## factor(id)7 -0.36246 0.30545 -1.187 0.2362
## factor(TGShort)Coral. -2.57419 0.35401 -7.272 2.64e-12 ***
## factor(TGShort)Invert. -0.27942 0.17003 -1.643 0.1013
## factor(TGShort)Plankt. 0.79255 1.41869 0.559 0.5768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Sigma link function: log
## Sigma Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2877 0.0384 33.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## No. of observations in the fit: 339
## Degrees of Freedom for the fit: 12
## Residual Deg. of Freedom: 327
## at cycle: 2
##
## Global Deviance: -608.4288
## AIC: -584.4288
## SBC: -538.5168
## ******************************************************************
## GAIG with k= 5.83
## GAIG with k= 5.83
## GAIG with k= 5.83
## Single term deletions for
## mu
##
## Model:
## CPUEw ~ factor(Drng) + factor(id) + factor(TGShort)
## Df AIC LRT Pr(Chi)
## <none> -584.43
## factor(Drng) 1 -586.03 0.3957 0.529316
## factor(id) 6 -587.09 9.3419 0.155245
## factor(TGShort) 3 -578.95 11.4739 0.009421 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Excluding depth as a factor
## GAMLSS-RS iteration 1: Global Deviance = 14.5504
## GAMLSS-RS iteration 2: Global Deviance = 14.5504
## minimum GAIC(k= 2 ) family: LNO
## minimum GAIC(k= 3.84 ) family: EXP
## minimum GAIC(k= 4.39 ) family: EXP
## GAIG with k= 4.39
## EXP LNO LOGNO LOGNO2 PARETO2 GP WEI3
## -90.41875 -89.61889 -89.61889 -89.61889 -89.12484 -89.12484 -87.04005
## WEI WEI2 GG GA GIG GB2 BCTo
## -87.04003 -87.03958 -86.56286 -86.42946 -84.14779 -83.26668 -82.94668
## BCT IG IGAMMA PARETO2o BCPEo BCCGo BCCG
## -81.76755 -79.32936 -74.70143 -12.60698 10.48574 4411.12730 NA
## exGAUS BCPE
## NA NA
## GAIG with k= 4.39
## GAMLSS-RS iteration 1: Global Deviance = -134.3253
## GAMLSS-RS iteration 2: Global Deviance = -134.3253
## GAIG with k= 4.39
## GAMLSS-RS iteration 1: Global Deviance = -134.3307
## GAMLSS-RS iteration 2: Global Deviance = -134.3307
## GAIG with k= 4.39
## GAMLSS-RS iteration 1: Global Deviance = -134.3253
## GAMLSS-RS iteration 2: Global Deviance = -134.3253
## GAIG with k= 4.39
## GAMLSS-RS iteration 1: Global Deviance = -106.0583
## GAMLSS-RS iteration 2: Global Deviance = -106.0583
## df AIC
## mod10a 10.00000 -114.32531
## mod7a 10.00149 -114.32232
## mod8a 11.00000 -112.33073
## mod12a 5.00000 -96.05828
## GAIG with k= 4.39
## ******************************************************************
## Family: c("EXP", "Exponential")
##
## Call: gamlss(formula = CPUEw ~ factor(id) + random(factor(survey)) +
## factor(TGShort), family = names(getOrder(t1, 3)[1]),
## data = na.omit(subset(c43, gear == "GN" & CPUEw > 0)))
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.2144 0.5723 -5.617 3.54e-07 ***
## factor(id)2 1.6471 0.5936 2.775 0.007058 **
## factor(id)3 3.6041 1.1609 3.105 0.002736 **
## factor(id)4 2.4232 0.6727 3.602 0.000581 ***
## factor(id)5 1.6098 0.6093 2.642 0.010125 *
## factor(id)6 0.9530 0.6331 1.505 0.136650
## factor(id)7 2.4219 0.6623 3.657 0.000486 ***
## factor(TGShort)Herb. -1.3321 0.3884 -3.429 0.001011 **
## factor(TGShort)Invert. -0.8166 0.4456 -1.832 0.071081 .
## factor(TGShort)Plankt. -0.5581 0.5908 -0.945 0.347993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## NOTE: Additive smoothing terms exist in the formulas:
## i) Std. Error for smoothers are for the linear effect only.
## ii) Std. Error for the linear terms maybe are not accurate.
## ------------------------------------------------------------------
## No. of observations in the fit: 81
## Degrees of Freedom for the fit: 10.00149
## Residual Deg. of Freedom: 70.99851
## at cycle: 2
##
## Global Deviance: -134.3253
## AIC: -114.3223
## SBC: -90.37426
## ******************************************************************
## GAIG with k= 4.39
## GAIG with k= 4.39
## GAIG with k= 4.39
## Single term deletions for
## mu
##
## Model:
## CPUEw ~ factor(id) + random(factor(survey)) + factor(TGShort)
## Df AIC LRT Pr(Chi)
## <none> -114.322
## factor(id) 6.0014820 -97.291 29.034 6.002e-05 ***
## random(factor(survey)) 0.0014933 -114.325 0.000 0.009631 **
## factor(TGShort) 3.0014485 -108.948 11.377 0.009866 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
####Hyperstability trap catches - max number of fish per trap Asymptotic histogram - no indication of reaching a plateau, only low probability of catching many fish.
Use GAM model and evaluate how CPUE by weight (CPUEw) was affected by the traits :
(excluding depth, area and survey)
## GAMLSS-RS iteration 1: Global Deviance = -533.6391
## GAMLSS-RS iteration 2: Global Deviance = -533.6391
## minimum GAIC(k= 2 ) family: BCPEo
## minimum GAIC(k= 3.84 ) family: BCPEo
## minimum GAIC(k= 5.8 ) family: BCPEo
## GAIG with k= 5.8
## BCPEo BCT BCTo GB2 IG BCCGo IGAMMA
## -1320.2608 -1222.8654 -1222.7411 -1178.6756 -1165.0550 -1161.7199 -1158.3079
## GG GIG LOGNO2 LOGNO LNO GA WEI3
## -1156.6036 -1152.5063 -1126.3848 -1126.3848 -1126.3848 -1071.0676 -1020.6140
## WEI WEI2 EXP GP PARETO2 PARETO2o BCCG
## -1020.6140 -1020.3713 -890.8462 -860.1424 -860.1424 NA NA
## exGAUS BCPE
## NA NA
## GAIG with k= 5.8
## GAMLSS-RS iteration 1: Global Deviance = -963.7931
## GAMLSS-RS iteration 2: Global Deviance = -1126.689
## GAMLSS-RS iteration 3: Global Deviance = -1245.823
## GAMLSS-RS iteration 4: Global Deviance = -1357.345
## GAMLSS-RS iteration 5: Global Deviance = -1383.994
## GAMLSS-RS iteration 6: Global Deviance = -1395.602
## GAMLSS-RS iteration 7: Global Deviance = -1403.267
## GAMLSS-RS iteration 8: Global Deviance = -1408.302
## GAMLSS-RS iteration 9: Global Deviance = -1411.131
## GAMLSS-RS iteration 10: Global Deviance = -1413.249
## GAMLSS-RS iteration 11: Global Deviance = -1414.883
## GAMLSS-RS iteration 12: Global Deviance = -1416.246
## GAMLSS-RS iteration 13: Global Deviance = -1417.419
## GAMLSS-RS iteration 14: Global Deviance = -1418.103
## GAMLSS-RS iteration 15: Global Deviance = -1417.253
## GAMLSS-RS iteration 16: Global Deviance = -1418.231
## GAMLSS-RS iteration 17: Global Deviance = -1414.322
## GAMLSS-RS iteration 18: Global Deviance = -1421.598
## GAMLSS-RS iteration 19: Global Deviance = -1418.595
## GAMLSS-RS iteration 20: Global Deviance = -1418.861
## GAIG with k= 5.8
## ******************************************************************
## Family: c("BCPEo", "Box-Cox Power Exponential-orig.")
##
## Call: gamlss(formula = CPUEn ~ TrophicLevel + factor(TGShort) +
## factor(WaterCol) + factor(DielActivity) + factor(Habitat) +
## factor(Gregariousness) + MaxLength, family = names(getOrder(t1,
## 3)[1]), data = na.omit(ct))
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value
## (Intercept) -2.6567135 0.0023312 -1139.647
## TrophicLevel -0.0545254 0.0002972 -183.479
## factor(TGShort)Coral. -0.2104276 0.0016784 -125.375
## factor(TGShort)Invert. -0.0363964 0.0012421 -29.302
## factor(TGShort)Plankt. 2.8405777 0.0028872 983.855
## factor(WaterCol)Demersal 0.0817585 0.0020562 39.763
## factor(WaterCol)pelagic non-site attached 0.0896340 0.0027945 32.075
## factor(WaterCol)pelagic site attached -2.4831072 0.0029956 -828.924
## factor(DielActivity)Night -0.0207855 0.0018257 -11.385
## factor(Habitat)Coral 2.5952503 0.0029202 888.729
## factor(Habitat)sand 0.3949726 0.0019661 200.894
## factor(Gregariousness)2 -0.0078488 0.0011942 -6.572
## factor(Gregariousness)3 0.0050428 0.0021788 2.314
## MaxLength 0.0004446 0.0001008 4.411
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## TrophicLevel < 2e-16 ***
## factor(TGShort)Coral. < 2e-16 ***
## factor(TGShort)Invert. < 2e-16 ***
## factor(TGShort)Plankt. < 2e-16 ***
## factor(WaterCol)Demersal < 2e-16 ***
## factor(WaterCol)pelagic non-site attached < 2e-16 ***
## factor(WaterCol)pelagic site attached < 2e-16 ***
## factor(DielActivity)Night < 2e-16 ***
## factor(Habitat)Coral < 2e-16 ***
## factor(Habitat)sand < 2e-16 ***
## factor(Gregariousness)2 2.08e-10 ***
## factor(Gregariousness)3 0.0213 *
## MaxLength 1.42e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Sigma link function: log
## Sigma Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6509 0.3242 2.007 0.0456 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Nu link function: identity
## Nu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8710 0.1941 -4.487 1.02e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Tau link function: log
## Tau Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.3799 0.1233 -11.19 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## No. of observations in the fit: 329
## Degrees of Freedom for the fit: 17
## Residual Deg. of Freedom: 312
## at cycle: 20
##
## Global Deviance: -1418.861
## AIC: -1384.861
## SBC: -1320.328
## ******************************************************************
## GAIG with k= 5.8
## GAIG with k= 5.8
## GAIG with k= 5.8
## GAIG with k= 5.8
## GAIG with k= 5.8
## GAIG with k= 5.8
## GAIG with k= 5.8
## Single term deletions for
## mu
##
## Model:
## CPUEn ~ TrophicLevel + factor(TGShort) + factor(WaterCol) + factor(DielActivity) +
## factor(Habitat) + factor(Gregariousness) + MaxLength
## Df AIC LRT Pr(Chi)
## <none> -1384.9
## TrophicLevel 1 -1385.7 1.2152 0.2702995
## factor(TGShort) 3 -1361.5 29.3473 1.893e-06 ***
## factor(WaterCol) 3 -1368.1 22.7719 4.505e-05 ***
## factor(DielActivity) 1 -1380.0 6.8626 0.0088020 **
## factor(Habitat) 2 -1362.3 26.5737 1.697e-06 ***
## factor(Gregariousness) 2 -1374.1 14.7961 0.0006125 ***
## MaxLength 1 -1385.2 1.6076 0.2048317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## ******************************************************************
## Summary of the Quantile Residuals
## mean = 0.21142
## variance = 1.052263
## coef. of skewness = 0.1719285
## coef. of kurtosis = 2.067135
## Filliben correlation coefficient = 0.9855425
## ******************************************************************
Evaluting catches of fish in relation to their traits. Should be evaluated by numbers, for single large fish not to dominate the results. The model based on CPUE by numbers (mod12, where surveys, area and depth are excluded) had lower AIC than models with numbers and fishing hours as a factor. Also, including survey as an ordinary factor led to lower AIC than including it as a random factor.
Model (BCPOe Box-Cox Power Exponential-orig. distribution, AIC: -1384.861, df:17) results show that numbers of fish caught (CPUE by numbers) was significantly affected by:
Presents the CPUE pr hr, for each management area and survey, standardized by the number of traps set in each area X survey combination.
Trap catches were dominated by carnivores, occuring in all area X survey combinations, followed by invertivores who also occurred everywhere, albeight in lower densities than the carnivores.
Cathces of planktivores and invertivores varied substantially between areas and surveys, in contrast to carnivores who were caught in all surveys and areas. This explains why invertivores and planktivores were signifant factors on the catches (GAM model of CPUEn with traits), while carnivores were not.
Area 2, 5 and 6 seem to have the highest diversity of trophic groups, but this has to be calculated as functional diversity, using the FD package (or similar).
## quartz_off_screen
## 2
A modified version of Figure 6 in Feb 2019 MS.
Basically a more complex version of the previous plot. Adds the depth dimension to the plot, and the surveys as colours.
Shows uniform catch rates of carnivores and invertivores from 0-50m.
All surveys seem to overlap, so no difference in trophich group depth dependent catch rates between the surveys.
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## GAMLSS-RS iteration 1: Global Deviance = 186.3385
## GAMLSS-RS iteration 2: Global Deviance = 186.3385
## minimum GAIC(k= 2 ) family: BCPEo
## minimum GAIC(k= 3.84 ) family: BCPEo
## minimum GAIC(k= 5.85 ) family: BCPEo
## GAIG with k= 5.85
## BCPEo BCPE WEI WEI3 WEI2 GG GB2 BCTo
## 190.2248 190.3103 218.5686 218.5686 218.5696 219.0720 225.8835 229.9747
## BCT BCCGo BCCG exGAUS GA LOGNO LNO LOGNO2
## 230.0487 236.0337 236.0926 250.7759 253.4726 258.1262 258.1262 258.1262
## IG GIG IGAMMA EXP PARETO2o GP PARETO2
## 258.5443 259.6889 263.1506 1684.4977 1695.0055 1722.6221 1722.6221
## GAIG with k= 5.8
## [1] "BCPEo"
## GAIG with k= 5.8
## GAMLSS-RS iteration 1: Global Deviance = 131.925
## GAMLSS-RS iteration 2: Global Deviance = 121.8234
## GAMLSS-RS iteration 3: Global Deviance = 120.5852
## GAMLSS-RS iteration 4: Global Deviance = 120.2275
## GAMLSS-RS iteration 5: Global Deviance = 120.1006
## GAMLSS-RS iteration 6: Global Deviance = 120.0508
## GAMLSS-RS iteration 7: Global Deviance = 120.0366
## GAMLSS-RS iteration 8: Global Deviance = 120.0287
## GAMLSS-RS iteration 9: Global Deviance = 120.0266
## GAMLSS-RS iteration 10: Global Deviance = 120.0251
## GAMLSS-RS iteration 11: Global Deviance = 120.0248
## GAIG with k= 5.8
## ******************************************************************
## Family: c("BCPEo", "Box-Cox Power Exponential-orig.")
##
## Call: gamlss(formula = TrophicLevel ~ depth + factor(id) +
## factor(survey), family = names(getOrder(t1, 3)[1]),
## data = na.omit(ct))
##
## Fitting method: RS()
##
## ------------------------------------------------------------------
## Mu link function: log
## Mu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3630905 0.0129179 105.519 <2e-16 ***
## depth -0.0002265 0.0001722 -1.315 0.1893
## factor(id)2 0.0096046 0.0110097 0.872 0.3836
## factor(id)3 0.0316831 0.0139916 2.264 0.0242 *
## factor(id)4 0.0002802 0.0133868 0.021 0.9833
## factor(id)5 0.0057113 0.0132689 0.430 0.6672
## factor(id)6 -0.0090797 0.0111079 -0.817 0.4143
## factor(id)7 0.0021158 0.0120088 0.176 0.8603
## factor(survey)2013002 -0.0001105 0.0041665 -0.027 0.9789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Sigma link function: log
## Sigma Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.57961 0.03129 -82.43 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Nu link function: identity
## Nu Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2984 0.4915 6.71 8.34e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## Tau link function: log
## Tau Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5043 0.1077 13.96 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ------------------------------------------------------------------
## No. of observations in the fit: 346
## Degrees of Freedom for the fit: 12
## Residual Deg. of Freedom: 334
## at cycle: 11
##
## Global Deviance: 120.0248
## AIC: 144.0248
## SBC: 190.1821
## ******************************************************************
## GAIG with k= 5.8
## GAIG with k= 5.8
## GAIG with k= 5.8
## Single term deletions for
## mu
##
## Model:
## TrophicLevel ~ depth + factor(id) + factor(survey)
## Df AIC LRT Pr(Chi)
## <none> 144.03
## depth 1 143.68 1.6516 0.19874
## factor(id) 6 144.89 12.8634 0.04526 *
## factor(survey) 1 142.03 0.0005 0.98153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The GAM Model (Box-Cox Power Exponential, AIC: 144.0248, df: 12) of trophic level by depth, area and surveys only showed area 3 and the intercept to be significant factors affecting the Trophic level of catches meaning that trophic level was similar in all areas except area 3 (and 1)
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## area spc deep.acc$spc gill.acc$spc
## 1 All 49 43 93
## 2 Area1 9 13 14
## 3 Area2 24 27 38
## 4 Area3 17 9 5
## 5 Area4 4 8 22
## 6 Area5 18 2 35
## 7 Area6 19 15 13
## 8 Area7 16 13 37
UPDATE March 2021. Including both traps and gillnet data in analysis.
We did not split the FD analysis by depth (<> 30m) as this would bias the shallow bin because that would include both gillnets and traps while the deep bin only included trap catches.
We used the same approach as Stuart-Smith et al. to exclude very few observations of a single species and excluded all species observations occuring in 2 or fewer stations (traps).
To avoid dbFD crashing ‘m’ was set in the ‘dbFD’ call (the number of PoCA axes kept as traits to do the FRic analysis). Several levels of ‘m’ were tested, and this could probably be tested further (to higher levels of ‘m’)
Results based on the output from the dbFD analysis:
Overall Fric (R2)= 0.533 (compared to 0.62 for traps only data)
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5331195
| Area | FEve | FDiv | FDis | RaoQ | |
|---|---|---|---|---|---|
| A1 | A1 | 0.796 | 0.889 | 0.269 | 0.09 |
| A2 | A2 | 0.735 | 0.85 | 0.282 | 0.087 |
| A3 | A3 | 0.7 | 0.873 | 0.237 | 0.063 |
| A4 | A4 | 0.669 | 0.905 | 0.183 | 0.038 |
| A5 | A5 | 0.62 | 0.827 | 0.235 | 0.069 |
| A6 | A6 | 0.842 | 0.738 | 0.222 | 0.063 |
| A7 | A7 | 0.737 | 0.841 | 0.221 | 0.068 |
Need to reduce ‘m’ to 9 to avoid crash:
Zero distance(s)Error in convhulln(tr.FRic, “FA”) : Received error code 2 from qhull. Qhull error: QH6114 qhull precision error: initial simplex is not convex. Distance=1.4e-16
Removing traits only improved the R2 by max 9.6% (by removing Maxlength). Therefore decided to keep all the traits in the model for Traps and Gillnets
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 22 PCoA axes (out of 32 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5838667
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 23 PCoA axes (out of 33 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5458381
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5167443
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.4948348
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 27 PCoA axes (out of 37 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5634173
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5033913
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5619108
| All | MaxLength | Trophic.Level | Trophic.group | Water.column | Habitat | Gregariousness | diel | |
|---|---|---|---|---|---|---|---|---|
| R2 | 0.533 | 0.584 | 0.546 | 0.517 | 0.495 | 0.563 | 0.503 | 0.562 |
| All | MaxLength | Trophic.Level | Trophic.group | Water.column | Habitat | Gregariousness | diel | |
|---|---|---|---|---|---|---|---|---|
| A1 | 0.090 | 0.109 | 0.103 | 0.075 | 0.072 | 0.107 | 0.082 | 0.094 |
| A2 | 0.087 | 0.109 | 0.099 | 0.066 | 0.065 | 0.107 | 0.076 | 0.105 |
| A3 | 0.063 | 0.075 | 0.068 | 0.046 | 0.050 | 0.086 | 0.051 | 0.078 |
| A4 | 0.038 | 0.045 | 0.043 | 0.047 | 0.034 | 0.030 | 0.029 | 0.047 |
| A5 | 0.069 | 0.092 | 0.079 | 0.056 | 0.051 | 0.079 | 0.062 | 0.076 |
| A6 | 0.063 | 0.075 | 0.078 | 0.054 | 0.050 | 0.082 | 0.056 | 0.065 |
| A7 | 0.068 | 0.073 | 0.077 | 0.063 | 0.050 | 0.078 | 0.068 | 0.078 |
For traps excluding traits had an even less effect than for the traps & gillnet analysis. The only removal of trait that increased R2 was for trophic level, but the increase was only 0.7%. Therefore the full model including all traits was used in the manuscript.
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8260286
| Area | FEve | FDiv | FDis | RaoQ | |
|---|---|---|---|---|---|
| A1 | A1 | 0.86 | 0.841 | 0.257 | 0.076 |
| A2 | A2 | 0.828 | 0.892 | 0.23 | 0.066 |
| A3 | A3 | 0.794 | 0.908 | 0.227 | 0.069 |
| A4 | A4 | 0.82 | 0.84 | 0.225 | 0.059 |
| A5 | A5 | 0.787 | 0.885 | 0.22 | 0.063 |
| A6 | A6 | 0.886 | 0.873 | 0.267 | 0.084 |
| A7 | A7 | 0.685 | 0.854 | 0.238 | 0.062 |
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 8 PCoA axes (out of 16 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8190764
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 5 PCoA axes (out of 14 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8316098
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.7674298
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8002454
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.80821
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.7906913
## Species x species distance matrix was not Euclidean. Lingoes correction was applied.
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8237468
| All | MaxLength | Trophic.Level | Trophic.group | Water.column | Habitat | Gregariousness | diel | |
|---|---|---|---|---|---|---|---|---|
| R2 | 0.826 | 0.819 | 0.832 | 0.767 | 0.8 | 0.808 | 0.791 | 0.824 |
| All | MaxLength | Trophic.Level | Trophic.group | Water.column | Habitat | Gregariousness | diel | |
|---|---|---|---|---|---|---|---|---|
| A1 | 0.076 | 0.095 | 0.078 | 0.066 | 0.066 | 0.100 | 0.059 | 0.087 |
| A2 | 0.066 | 0.082 | 0.070 | 0.063 | 0.050 | 0.083 | 0.060 | 0.073 |
| A3 | 0.069 | 0.079 | 0.069 | 0.068 | 0.056 | 0.094 | 0.054 | 0.075 |
| A4 | 0.059 | 0.081 | 0.060 | 0.058 | 0.048 | 0.075 | 0.044 | 0.067 |
| A5 | 0.063 | 0.073 | 0.059 | 0.062 | 0.049 | 0.083 | 0.055 | 0.072 |
| A6 | 0.084 | 0.097 | 0.087 | 0.075 | 0.071 | 0.111 | 0.071 | 0.091 |
| A7 | 0.062 | 0.077 | 0.057 | 0.055 | 0.051 | 0.080 | 0.059 | 0.073 |
This table combines CPUE, traits pr gear type, species density and RaoQ into one results-table.
| area | TrapCPUEw | GillnetCPUEw | TrapCPUEn | GillnetCPUEn | TrophicL_Traps | TrophicL_Gillnets | Greg_Traps | Greg_Gillnets | Species_traps | Species_gillnets | RaoQ_All | RaoQ_Traps |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.117 | 1.652 | 0.060 | 0.700 | 3.939 | 4.075 | 1.488 | 2.438 | 16 | 14 | 0.090 | 0.076 |
| 2 | 0.150 | 1.176 | 0.087 | 1.366 | 3.914 | 3.933 | 1.541 | 2.295 | 34 | 40 | 0.087 | 0.066 |
| 3 | 0.136 | 0.415 | 0.076 | 0.455 | 3.968 | 4.140 | 1.556 | 2.500 | 23 | 5 | 0.063 | 0.069 |
| 4 | 0.059 | 0.698 | 0.067 | 1.001 | 3.875 | 4.262 | 1.385 | 1.957 | 11 | 23 | 0.038 | 0.059 |
| 5 | 0.079 | 0.904 | 0.090 | 1.616 | 3.831 | 3.871 | 1.685 | 2.200 | 18 | 36 | 0.069 | 0.063 |
| 6 | 0.135 | 0.319 | 0.072 | 0.975 | 3.826 | 4.020 | 1.602 | 2.263 | 24 | 13 | 0.063 | 0.084 |
| 7 | 0.098 | 0.681 | 0.075 | 1.208 | 3.806 | 3.716 | 1.650 | 1.778 | 23 | 38 | 0.068 | 0.062 |